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Book chapter - Peer-reviewed, 2022

Genome and Environment Based Prediction Models and Methods of Complex Traits Incorporating Genotype × Environment Interaction

Crossa, José; Montesinos-López, Osval Antonio; Perez-Rodriguez, Paulino; Costa-Neto, Germano; Fritsche-Neto, Roberto; Ortiz, Rodomiro; Martini, Johannes W. R.; Lillemo, Morten; Montesinos-López, Abelardo; Jarquin, Diego; Breseghello, Flavio; Cuevas, Jaime; Rincent, Renaud

Abstract

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.

Keywords

Genome-enabled prediction; Genomic selection; Models with G × E interaction; Plant breeding

Published in

Methods in Molecular Biology
2022, number: 2467, pages: 245-283
Book title: Complex Trait Prediction : Methods and Protocols
ISBN: 978-1-0716-2204-9, eISBN: 978-1-0716-2205-6
Publisher: Springer

Authors' information

Crossa, José
International Maize and Wheat Improvement Center (CIMMYT)
Montesinos-López, Osval Antonio
Universidad de Colima
Perez-Rodriguez, Paulino
Colegio de Postgraduados - Mexico
Costa-Neto, Germano
University of Sao Paulo
Fritsche-Neto, Roberto
University of Sao Paulo
Ortiz, Rodomiro (Ortiz Rios, Rodomiro Octavio)
Swedish University of Agricultural Sciences, Department of Plant Breeding
Martini, Johannes W. R.
International Maize and Wheat Improvement Center (CIMMYT)
Lillemo, Morten
Norwegian University of Life Sciences (NMBU)
Montesinos-López, Abelardo
Universidad de Guadalajara
Jarquin, Diego
University of Nebraska-Lincoln
Breseghello, Flavio
Embrapa Rice and Beans
Cuevas, Jaime
Universidad de Quintana Roo
Rincent, Renaud
National Research Institute for Agriculture, Food and Environment (INRAE)

Sustainable Development Goals

SDG2 Zero hunger

UKÄ Subject classification

Genetics and Breeding
Agricultural Science
Plant Biotechnology

Publication Identifiers

DOI: https://doi.org/10.1007/978-1-0716-2205-6_9

URI (permanent link to this page)

https://res.slu.se/id/publ/116780