A joint learning approach for genomic prediction in polyploid grasses.

• Autor(a) principal: AONO, A. H.
• Data de Publicação: 2022
• Outros Autores: 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
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice)
• Texto Completo: http://www.alice.cnptia.embrapa.br/alice/handle/doc/1150365; https://doi.org/10.1038/s41598-022-16417-7
• topic: Cana de Açúcar; Gramínea Forrageira; Recurso Genético; Forage grasses; Genetic resources; Plant breeding; Poaceae; Polyploidy; Saccharum; Sugarcane

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.