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dc.contributor.authorCROSSA, J.
dc.contributor.authorMONTESINOS-LÓPEZ, O. A.
dc.contributor.authorPÉREZ-RODRÍGUEZ, P.
dc.contributor.authorCOSTA-NETO, G.
dc.contributor.authorFRITSCHE-NETO, R.
dc.contributor.authorORTIZ, R.
dc.contributor.authorMARTINI, J. W. R.
dc.contributor.authorLILLEMO, M.
dc.contributor.authorMONTESINOS-LÓPEZ, A.
dc.contributor.authorJARQUIN, D.
dc.contributor.authorBRESEGHELLO, F.
dc.contributor.authorCUEVAS, J.
dc.contributor.authorRINCENT, R.
dc.date.accessioned2022-05-30T05:00:48Z-
dc.date.available2022-05-30T05:00:48Z-
dc.date.created2022-05-29
dc.date.issued2022
dc.identifier.citationIn: AHMADI, N.; BARTHOLOME, J. (ed.). Genomic prediction of complex traits: methods and protocols. New York: Humana Press, 2022.
dc.identifier.isbn978-1-0716-2205-6
dc.identifier.urihttp://www.alice.cnptia.embrapa.br/alice/handle/doc/1143533-
dc.descriptionGenomic-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.
dc.language.isoeng
dc.relation.ispartofseries(Methods in Molecular Biology).
dc.rightsopenAccess
dc.subjectGenomic selection
dc.subjectGenome-enabled prediction
dc.subjectModels with G x E interaction
dc.titleGenome and environment based prediction models and methods of complex traits incorporating genotype × environment interaction.
dc.typeParte de livro
dc.subject.thesagroMelhoramento Genético Vegetal
dc.subject.thesagroGenótipo
dc.subject.thesagroInteração Genética
dc.subject.nalthesaurusGenome
dc.subject.nalthesaurusGenomics
dc.subject.nalthesaurusPlant breeding
dc.subject.nalthesaurusGenotype-environment interaction
dc.format.extent2p. 245-283.
riaa.ainfo.id1143533
riaa.ainfo.lastupdate2022-05-29
dc.identifier.doihttps://doi.org/10.1007/978-1-0716-2205-6_9
dc.contributor.institutionJOSE CROSSA, CIMMYT; OSVAL ANTONIO MONTESINOS-LOPEZ, UNIVERSIDAD DE COLIMA, México; PAULINO PEREZ-RODRIGUEZ, COLEGIO DE POSTGRADUADOS, Montecillos-Mexico; GERMANO COSTA-NETO, ESALQ; ROBERTO FRITSCHE-NETO, ESALQ; RODOMIRO ORTIZ, SWEDISH UNIVERSITY OF AGRICULTURAL SCIENCES, Alnarp-Sweden; JOHANNES W. R. MARTINI, CIMMYT; MORTEN LILLEMO, NORWEGIAN UNIVERSITY OF LIFE SCIENCES, Norway; ABELARDO MONTESINOS-LOPEZ, CENTRO DE INVESTIGACIÓN EN MATEMÁTICAS, Guanajuato-Mexico; DIEGO JARQUIN, UNIVERSITY OF NEBRASKA, Lincoln-NE; FLAVIO BRESEGHELLO, CNPAF; JAIME CUEVAS, UNIVERSIDAD DE QUINTANA ROO, Quintana Roo-Mexico; RENAUD RINCENT, INRAE, Clermont-Ferrand-France.
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