Multi-environment genomic selection in rice elite breeding lines

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cg.authorship.typesCGIAR and developing country instituteen
cg.authorship.typesCGIAR and advanced research instituteen
cg.contributor.affiliationCentre de Coopération Internationale en Recherche Agronomique Pour le Développementen
cg.contributor.affiliationInternational Rice Research Instituteen
cg.contributor.affiliationUniversity of the Philippinesen
cg.contributor.affiliationRiceTec, Inc.en
cg.contributor.crpExcellence in Breedingen
cg.contributor.donorBill & Melinda Gates Foundationen
cg.contributor.donorCGIAR Trust Funden
cg.contributor.donorAgropolis Fondationen
cg.contributor.donorSoutheast Asian Regional Center for Graduate Study and Research in Agricultureen
cg.contributor.initiativeAccelerated Breedingen
cg.creator.identifierHolden Verdeprado: 0000-0003-4232-5172en
cg.creator.identifierNdayiragije Alexis: 0000-0002-2739-1019en
cg.creator.identifierJean Christophe Glaszmann: 0000-0001-9918-875Xen
cg.creator.identifierJoshua N. Cobb: 0000-0002-1732-2378en
cg.creator.identifierJérôme Bartholomé: 0000-0002-0855-3828en
cg.howPublishedFormally Publisheden
cg.identifier.doihttps://doi.org/10.21203/rs.3.rs-2133066/v1en
cg.journalResearch Squareen
cg.speciesOryza sativaen
cg.subject.actionAreaGenetic Innovationen
cg.subject.impactAreaNutrition, health and food securityen
dc.contributor.authorNguyen, Van Hieuen
dc.contributor.authorMorantte, Rose Imee Zhellaen
dc.contributor.authorLopena, Vitalianoen
dc.contributor.authorVerdeprado, Holdenen
dc.contributor.authorMurori, Rosemaryen
dc.contributor.authorNdayiragije, Alexisen
dc.contributor.authorKatiyar, Sanjayen
dc.contributor.authorIslam, Md Rafiqulen
dc.contributor.authorJuma, Roselyne U.en
dc.contributor.authorGalvez, Haydeen
dc.contributor.authorGlaszmann, Jean-Christopheen
dc.contributor.authorCobb, Joshua N.en
dc.contributor.authorBartholomé, Jérômeen
dc.date.accessioned2023-01-20T12:31:28Zen
dc.date.available2023-01-20T12:31:28Zen
dc.identifier.urihttps://hdl.handle.net/10568/127686
dc.titleMulti-environment genomic selection in rice elite breeding linesen
dcterms.abstractAbstract Background: Assessing the performance of elite lines in target environments is essential for breeding programs to select the most relevant genotypes. One of the main complexities in this task resides in accounting for the genotype by environment interactions. Genomic prediction models that integrate information from multi-environment trials and environmental covariates can be efficient tools in this context. The objective of this study was to assess the predictive ability of different genomic prediction models to optimize the use of multi- environment information. We used 111 elite breeding lines representing the diversity of the International Rice Research Institute (IRRI) breeding program for irrigated ecosystems. The lines were evaluated for three traits (days to flowering, plant height, and grain yield) in 15 environments in Asia and Africa and genotyped with 882 SNP markers. We evaluated the efficiency of genomic prediction to predict untested environments using seven multi-environment models and three cross-validation scenarios. Results: The elite lines were found to belong to the indica group and more specifically the indica-1B subgroup which gathered improved material originating from the Green Revolution. Phenotypic correlations between environments were high for days to flowering and plant height (33% and 54% of pairwise correlation greater than 0.5 ) but low for grain yield (lower than 0.2 in most cases). Clustering analyses based on environmental covariates separated Asia’s and Africa's environments into different clusters or subclusters. The predictive abilities ranged from 0.06 to 0.79 for days to flowering, 0.25 to 0.88 for plant height, and -0.29 to 0.62 for grain yield. We found that models integrating genotype-by-environment interaction effects did not perform significantly better than models integrating only main effects (genotypes and environment or environmental covariates). The different cross-validation scenarios showed that, in most cases, the use of all available environments gave better results than a subset. Conclusion: Multi-environment genomic prediction models with main effects were sufficient for accurate phenotypic prediction of elite lines in targeted environments. The recommendation for the breeders is to use simple multi-environment models with all available information for routine application in breeding programs.en
dcterms.accessRightsOpen Accessen
dcterms.audienceCGIARen
dcterms.audienceDonorsen
dcterms.audienceScientistsen
dcterms.bibliographicCitationNguyen, V. H., Morantte, R.I.Z., Lopena, V., Verdeprado, H., Murori, R., Ndayiragije, A., Katiyar, S. et al. 2022.Multi-environment genomic selection in rice elite breeding lines. (2022).en
dcterms.extent1-21en
dcterms.issued2022-10-10en
dcterms.languageenen
dcterms.licenseCC-BY-4.0en
dcterms.publisherResearch Square Platform LLCen
dcterms.subjectriceen
dcterms.subjectresearchen
dcterms.typePreprinten

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