Author:
Aono Alexandre Hild,Ferreira Rebecca Caroline Ulbricht,Moraes Aline da Costa Lima,Lara Letícia Aparecida de Castro,Pimenta Ricardo José Gonzaga,Costa Estela Araujo,Pinto Luciana Rossini,Landell Marcos Guimarães de Andrade,Santos Mateus Figueiredo,Jank Liana,Barrios Sanzio Carvalho Lima,do Valle Cacilda Borges,Chiari Lucimara,Garcia Antonio Augusto Franco,Kuroshu Reginaldo Massanobu,Lorena Ana Carolina,Gorjanc Gregor,de Souza Anete Pereira
Abstract
AbstractPoaceae, 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 different cross-validation scenarios. By combining classification 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.
Funder
Fundação de Amparo à Pesquisa do Estado de São Paulo
Coordenação de Aperfeiçoamento de Pessoal de Nível Superior
Conselho Nacional de Desenvolvimento Científico e Tecnológico
Publisher
Springer Science and Business Media LLC
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