Título:	A joint learning approach for genomic prediction in polyploid grasses.
Autoria:	AONO, A. H. FERREIRA, R. C. U. MORAES, A. da C. L. LARA, L. A. de C. PIMENTA, R. J. G. COSTA, E. A. PINTO, L. R. LANDELL, M. G. de A. SANTOS, M. F. JANK, L. BARRIOS, S. C. L. VALLE, C. B. CHIARI, L. GARCIA, A. A. F. KUROSHU, R. M. LORENA, A. C. GORJANC, G. SOUZA, A. P. de
Afiliação:	ALEXANDRE HILD AONO, UNIVERSIDADE DE CAMPINAS, UNIVERSITY OF EDINBURGH REBECCA CAROLINE ULBRICHT FERREIRA, UNIVERSDIDADE DE CAMPINAS ALINE DA COSTA LIMA MORAES, UNIVERSIDADE DE CAMPINAS LETÍCIA APARECIDA DE CASTRO LARA, ESCOLA SUPERIOR DE AGRICULTURA "LUIZ DE QUEIROZ" RICARDO JOSÉ GONZAGA PIMENTA, UNIVERSIDADE DE CAMPINAS ESTELAARAUJO COSTA, UNIVEDRSIDADE FEDERAL DE SÃO PAULO LUCIANA ROSSINI PINTO, INSTITUTO AGRONÔMICO DE CAMPINAS MARCOS GUIMARÃES DE ANDRADE LANDELL, INSTITUTO AGRONÔMICO DE CAMPINAS MATEUS FIGUEIREDO SANTOS, CNPGC LIANA JANK, CNPGC SANZIO CARVALHO LIMA BARRIOS, CNPGC CACILDA BORGES DO VALLE, CNPGC LUCIMARA CHIARI, CNPGC ANTONIO AUGUSTO FRANCO GARCIA, ESCOLA SUPERIOR DE AGRICULTURA "LUIZ DE QUEIROZ" REGINALDO MASSANOBU KUROSHU, UNIVERSIDADE FERDERAL DE SÃO PAULO ANA CAROLINA LORENA, INSTITUTO TECNOLÓGICO DE AERONÁUTICA GREGOR GORJANC, UNIVERSITY OF EDINBURGH ANETE PEREIRA DE SOUZA, UNIVERSIDADE DE CAMPINAS.
Ano de publicação:	2022
Referência:	Scientific Reports, 12, article 12499, 2022.
Páginas:	17 p.
Conteúdo:	Poaceae, 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.
Thesagro:	Cana de Açúcar Gramínea Forrageira Recurso Genético
NAL Thesaurus:	Forage grasses Genetic resources Plant breeding Poaceae Polyploidy Saccharum Sugarcane
ISSN:	2045-2322
Digital Object Identifier:	https://doi.org/10.1038/s41598-022-16417-7
Tipo do material:	Artigo de periódico
Acesso:	openAccess
Aparece nas coleções:	Artigo em periódico indexado (CNPGC)