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

Crossa, José and Montesinos-López, Osval Antonio and Perez-Rodriguez, Paulino and Costa-Neto, Germano and Fritsche-Neto, Roberto and Ortiz, Rodomiro and Martini, Johannes W. R. and Lillemo, Morten and Montesinos-López, Abelardo and Jarquin, Diego and Breseghello, Flavio and Cuevas, Jaime and Rincent, Renaud (2022). Genome and Environment Based Prediction Models and Methods of Complex Traits Incorporating Genotype × Environment Interaction. I/In: Complex Trait Prediction : Methods and Protocols. Sid./p. 245-283. Methods in Molecular Biology (2467). Springer
ISBN 978-1-0716-2204-9
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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.

Authors/Creators:Crossa, José and Montesinos-López, Osval Antonio and Perez-Rodriguez, Paulino and Costa-Neto, Germano and Fritsche-Neto, Roberto and Ortiz, Rodomiro and Martini, Johannes W. R. and Lillemo, Morten and Montesinos-López, Abelardo and Jarquin, Diego and Breseghello, Flavio and Cuevas, Jaime and Rincent, Renaud
Title:Genome and Environment Based Prediction Models and Methods of Complex Traits Incorporating Genotype × Environment Interaction
Series Name/Journal:Methods in Molecular Biology
Year of publishing :2022
Number:2467
Page range:245-283
Number of Pages:39
Publisher:Springer
ISBN for printed version:978-1-0716-2204-9
ISBN for electronic version:978-1-0716-2205-6
ISSN:1064-3745
Language:English
Publication Type:Book Chapter
Article category:Scientific peer reviewed
Version:Published version
Copyright:Creative Commons: Attribution 4.0
Full Text Status:Public
Subjects:(A) Swedish standard research categories 2011 > 4 Agricultural Sciences > 401 Agricultural, Forestry and Fisheries > Agricultural Science
(A) Swedish standard research categories 2011 > 4 Agricultural Sciences > 404 Agricultural Biotechnology > Genetics and Breeding
(A) Swedish standard research categories 2011 > 4 Agricultural Sciences > 404 Agricultural Biotechnology > Plant Biotechnology
Keywords:Genome-enabled prediction, Genomic selection, Models with G × E interaction, Plant breeding
URN:NBN:urn:nbn:se:slu:epsilon-p-116780
Permanent URL:
http://urn.kb.se/resolve?urn=urn:nbn:se:slu:epsilon-p-116780
Additional ID:
Type of IDID
DOI10.1007/978-1-0716-2205-6_9
ID Code:27641
Faculty:LTV - Fakulteten för landskapsarkitektur, trädgårds- och växtproduktionsvetenskap
Department:(LTJ, LTV) > Department of Plant Breeding (from 130101)
Deposited By: SLUpub Connector
Deposited On:27 Apr 2022 07:25
Metadata Last Modified:27 Apr 2022 07:31

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