| Full text | |
| Author(s): Show less - |
Aono, Alexandre Hild
;
Ulbricht Ferreira, Rebecca Caroline
;
Lima Moraes, Aline da Costa
;
de Castro Lara, Leticia Aparecida
;
Gonzaga Pimenta, Ricardo Jose
;
Costa, Estela Araujo
;
Pinto, Luciana Rossini
;
de Andrade Landell, Marcos Guimaraes
;
Santos, Mateus Figueiredo
;
Jank, Liana
;
Lima Barrios, Sanzio Carvalho
;
do Valle, Cacilda Borges
;
Chiari, Lucimara
;
Franco Garcia, Antonio Augusto
;
Kuroshu, Reginaldo Massanobu
;
Lorena, Ana Carolina
;
Gorjanc, Gregor
;
de Souza, Anete Pereira
Total Authors: 18
|
| Document type: | Journal article |
| Source: | SCIENTIFIC REPORTS; v. 12, n. 1, p. 17-pg., 2022-07-21. |
| Abstract | |
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 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. (AU) | |
| FAPESP's process: | 19/26858-8 - Simulation of plant complex trait architectures using deep neural networks |
| Grantee: | Alexandre Hild Aono |
| Support Opportunities: | Scholarships abroad - Research Internship - Doctorate (Direct) |
| FAPESP's process: | 18/19219-6 - Detection of genes of agronomic interest and involved in heterosis in Urochloa spp. |
| Grantee: | Rebecca Caroline Ulbricht Ferreira |
| Support Opportunities: | Scholarships in Brazil - Post-Doctoral |
| FAPESP's process: | 19/21682-9 - Associative mapping and transcriptomic in the investigation of sugarcane yellow leaf virus resistance |
| Grantee: | Ricardo José Gonzaga Pimenta |
| Support Opportunities: | Scholarships in Brazil - Doctorate (Direct) |
| FAPESP's process: | 19/03232-6 - Genome wide selection in sugarcane using machine learning and complex networks for economically important traits |
| Grantee: | Alexandre Hild Aono |
| Support Opportunities: | Scholarships in Brazil - Doctorate (Direct) |