A joint learning approach for genomic prediction in polyploid grasses.

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dc.contributorALEXANDRE HILD AONO, UNIVERSIDADE DE CAMPINAS, UNIVERSITY OF EDINBURGH
dc.contributorREBECCA CAROLINE ULBRICHT FERREIRA, UNIVERSDIDADE DE CAMPINAS
dc.contributorALINE DA COSTA LIMA MORAES, UNIVERSIDADE DE CAMPINAS
dc.contributorLETÍCIA APARECIDA DE CASTRO LARA, ESCOLA SUPERIOR DE AGRICULTURA "LUIZ DE QUEIROZ"
dc.contributorRICARDO JOSÉ GONZAGA PIMENTA, UNIVERSIDADE DE CAMPINAS
dc.contributorESTELAARAUJO COSTA, UNIVEDRSIDADE FEDERAL DE SÃO PAULO
dc.contributorLUCIANA ROSSINI PINTO, INSTITUTO AGRONÔMICO DE CAMPINAS
dc.contributorMARCOS GUIMARÃES DE ANDRADE LANDELL, INSTITUTO AGRONÔMICO DE CAMPINAS
dc.contributorMATEUS FIGUEIREDO SANTOS, CNPGC
dc.contributorLIANA JANK, CNPGC
dc.contributorSANZIO CARVALHO LIMA BARRIOS, CNPGC
dc.contributorCACILDA BORGES DO VALLE, CNPGC
dc.contributorLUCIMARA CHIARI, CNPGC
dc.contributorANTONIO AUGUSTO FRANCO GARCIA, ESCOLA SUPERIOR DE AGRICULTURA "LUIZ DE QUEIROZ"
dc.contributorREGINALDO MASSANOBU KUROSHU, UNIVERSIDADE FERDERAL DE SÃO PAULO
dc.contributorANA CAROLINA LORENA, INSTITUTO TECNOLÓGICO DE AERONÁUTICA
dc.contributorGREGOR GORJANC, UNIVERSITY OF EDINBURGH
dc.contributorANETE PEREIRA DE SOUZA, UNIVERSIDADE DE CAMPINAS.
dc.creatorAONO, A. H.
dc.creatorFERREIRA, R. C. U.
dc.creatorMORAES, A. da C. L.
dc.creatorLARA, L. A. de C.
dc.creatorPIMENTA, R. J. G.
dc.creatorCOSTA, E. A.
dc.creatorPINTO, L. R.
dc.creatorLANDELL, M. G. de A.
dc.creatorSANTOS, M. F.
dc.creatorJANK, L.
dc.creatorBARRIOS, S. C. L.
dc.creatorVALLE, C. B.
dc.creatorCHIARI, L.
dc.creatorGARCIA, A. A. F.
dc.creatorKUROSHU, R. M.
dc.creatorLORENA, A. C.
dc.creatorGORJANC, G.
dc.creatorSOUZA, A. P. de
dc.date2022-12-27T15:01:28Z
dc.date2022-12-27T15:01:28Z
dc.date2022-12-27
dc.date2022
dc.date.accessioned2026-07-07T03:41:53Z
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.format17 p.
dc.identifierScientific Reports, 12, article 12499, 2022.
dc.identifier2045-2322
dc.identifierhttp://www.alice.cnptia.embrapa.br/alice/handle/doc/1150365
dc.identifierhttps://doi.org/10.1038/s41598-022-16417-7
dc.identifier.urihttp://hdl.handle.net/123456789/441131
dc.languageeng
dc.rightsopenAccess
dc.subjectCana de Açúcar
dc.subjectGramínea Forrageira
dc.subjectRecurso Genético
dc.subjectForage grasses
dc.subjectGenetic resources
dc.subjectPlant breeding
dc.subjectPoaceae
dc.subjectPolyploidy
dc.subjectSaccharum
dc.subjectSugarcane
dc.titleA joint learning approach for genomic prediction in polyploid grasses.
dc.typeArtigo de periódico

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