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dc.contributor.authorAONO, A. H.
dc.contributor.authorFERREIRA, R. C. U.
dc.contributor.authorMORAES, A. da C. L.
dc.contributor.authorLARA, L. A. de C.
dc.contributor.authorPIMENTA, R. J. G.
dc.contributor.authorCOSTA, E. A.
dc.contributor.authorPINTO, L. R.
dc.contributor.authorLANDELL, M. G. de A.
dc.contributor.authorSANTOS, M. F.
dc.contributor.authorJANK, L.
dc.contributor.authorBARRIOS, S. C. L.
dc.contributor.authorVALLE, C. B.
dc.contributor.authorCHIARI, L.
dc.contributor.authorGARCIA, A. A. F.
dc.contributor.authorKUROSHU, R. M.
dc.contributor.authorLORENA, A. C.
dc.contributor.authorGORJANC, G.
dc.contributor.authorSOUZA, A. P. de
dc.date.accessioned2022-12-27T15:01:28Z-
dc.date.available2022-12-27T15:01:28Z-
dc.date.created2022-12-27
dc.date.issued2022
dc.identifier.citationScientific Reports, 12, article 12499, 2022.
dc.identifier.issn2045-2322eng
dc.identifier.urihttp://www.alice.cnptia.embrapa.br/alice/handle/doc/1150365-
dc.descriptionPoaceae, among the most abundant plant families, includes many economically important polyploid species, such as forage grasses and sugarcane (Saccharum spp.). These species have elevated genomic complexities and limited genetic resources, hindering the application of marker-assisted selection strategies. Currently, the most promising approach for increasing genetic gains in plant breeding is genomic selection. However, due to the polyploidy nature of these polyploid species, more accurate models for incorporating genomic selection into breeding schemes are needed. This study aims to develop a machine learning method by using a joint learning approach to predict complex traits from genotypic data. Biparental populations of sugarcane and two species of forage grasses (Urochloa decumbens, Megathyrsus maximus) were genotyped, and several quantitative traits were measured. High-quality markers were used to predict several traits in diferent cross-validation scenarios. By combining classifcation and regression strategies, we developed a predictive system with promising results. Compared with traditional genomic prediction methods, the proposed strategy achieved accuracy improvements exceeding 50%. Our results suggest that the developed methodology could be implemented in breeding programs, helping reduce breeding cycles and increase genetic gains.
dc.language.isoeng
dc.rightsopenAccess
dc.titleA joint learning approach for genomic prediction in polyploid grasses.
dc.typeArtigo de periódico
dc.subject.thesagroCana de Açúcar
dc.subject.thesagroGramínea Forrageira
dc.subject.thesagroRecurso Genético
dc.subject.nalthesaurusForage grasses
dc.subject.nalthesaurusGenetic resources
dc.subject.nalthesaurusPlant breeding
dc.subject.nalthesaurusPoaceae
dc.subject.nalthesaurusPolyploidy
dc.subject.nalthesaurusSaccharum
dc.subject.nalthesaurusSugarcane
dc.format.extent217 p.
riaa.ainfo.id1150365
riaa.ainfo.lastupdate2022-12-27
dc.identifier.doihttps://doi.org/10.1038/s41598-022-16417-7
dc.contributor.institutionALEXANDRE HILD AONO, UNIVERSIDADE DE CAMPINAS, UNIVERSITY OF EDINBURGH
dc.contributor.institutionREBECCA CAROLINE ULBRICHT FERREIRA, UNIVERSDIDADE DE CAMPINASeng
dc.contributor.institutionALINE DA COSTA LIMA MORAES, UNIVERSIDADE DE CAMPINASeng
dc.contributor.institutionLETÍCIA APARECIDA DE CASTRO LARA, ESCOLA SUPERIOR DE AGRICULTURA "LUIZ DE QUEIROZ"eng
dc.contributor.institutionRICARDO JOSÉ GONZAGA PIMENTA, UNIVERSIDADE DE CAMPINASeng
dc.contributor.institutionESTELAARAUJO COSTA, UNIVEDRSIDADE FEDERAL DE SÃO PAULOeng
dc.contributor.institutionLUCIANA ROSSINI PINTO, INSTITUTO AGRONÔMICO DE CAMPINASeng
dc.contributor.institutionMARCOS GUIMARÃES DE ANDRADE LANDELL, INSTITUTO AGRONÔMICO DE CAMPINASeng
dc.contributor.institutionMATEUS FIGUEIREDO SANTOS, CNPGCeng
dc.contributor.institutionLIANA JANK, CNPGCeng
dc.contributor.institutionSANZIO CARVALHO LIMA BARRIOS, CNPGCeng
dc.contributor.institutionCACILDA BORGES DO VALLE, CNPGCeng
dc.contributor.institutionLUCIMARA CHIARI, CNPGCeng
dc.contributor.institutionANTONIO AUGUSTO FRANCO GARCIA, ESCOLA SUPERIOR DE AGRICULTURA "LUIZ DE QUEIROZ"eng
dc.contributor.institutionREGINALDO MASSANOBU KUROSHU, UNIVERSIDADE FERDERAL DE SÃO PAULOeng
dc.contributor.institutionANA CAROLINA LORENA, INSTITUTO TECNOLÓGICO DE AERONÁUTICAeng
dc.contributor.institutionGREGOR GORJANC, UNIVERSITY OF EDINBURGHeng
dc.contributor.institutionANETE PEREIRA DE SOUZA, UNIVERSIDADE DE CAMPINAS.eng
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