| Journal: | Scientia Agricola |
| Database: | PERIÓDICA |
| System number: | 000455527 |
| ISSN: | 0103-9016 |
| Authors: | Sousa, Ithalo Coelho de1 Nascimento, Moysés1 Silva, Gabi Nunes2 Nascimento, Ana Carolina Campana1 Cruz, Cosme Damião3 Silva, Fabyano Fonseca e4 Almeida, Dênia Pires de5 Pestana, Kátia Nogueira6 Azevedo, Camila Ferreira1 Zambolim, Laércio7 Caixeta, Eveline Teixeira8 |
| Institutions: | 1Universidade Federal de Vicosa, Departamento de Estatistica, Vicosa, Minas Gerais. Brasil 2Universidade Federal de Rondonia, Departamento de Matematica e Estatistica, Ji-Parana, Rondonia. Brasil 3Universidade Federal de Vicosa, Departamento de Biologia Geral, Vicosa, Minas Gerais. Brasil 4Universidade Federal de Vicosa, Departamento de Zootecnia, Vicosa, Minas Gerais. Brasil 5Universidade Federal de Vicosa, Instituto de Biotecnologia Aplicada a Agropecuaria, Vicosa, Minas Gerais. Brasil 6Empresa Brasileira de Pesquisa Agropecuaria, Mandioca e Fruticultura Tropical, Cruz das Almas, Bahia. Brasil 7Universidade Federal de Vicosa, Departamento de Fitopatologia, Vicosa, Minas Gerais. Brasil 8Empresa Brasileira de Pesquisa Agropecuaria, Cafe, Brasilia, Distrito Federal. Brasil |
| Year: | 2021 |
| Volumen: | 78 |
| Number: | 4 |
| Country: | Brasil |
| Language: | Inglés |
| Document type: | Artículo |
| Approach: | Experimental, analítico |
| English abstract | Genomic selection (GS) emphasizes the simultaneous prediction of the genetic effects of thousands of scattered markers over the genome. Several statistical methodologies have been used in GS for the prediction of genetic merit. In general, such methodologies require certain assumptions about the data, such as the normality of the distribution of phenotypic values. To circumvent the non-normality of phenotypic values, the literature suggests the use of Bayesian Generalized Linear Regression (GBLASSO). Another alternative is the models based on machine learning, represented by methodologies such as Artificial Neural Networks (ANN), Decision Trees (DT) and related possible refinements such as Bagging, Random Forest and Boosting. This study aimed to use DT and its refinements for predicting resistance to orange rust in Arabica coffee. Additionally, DT and its refinements were used to identify the importance of markers related to the characteristic of interest. The results were compared with those from GBLASSO and ANN. Data on coffee rust resistance of 245 Arabica coffee plants genotyped for 137 markers were used. The DT refinements presented equal or inferior values of Apparent Error Rate compared to those obtained by DT, GBLASSO, and ANN. Moreover, DT refinements were able to identify important markers for the characteristic of interest. Out of 14 of the most important markers analyzed in each methodology, 9.3 markers on average were in regions of quantitative trait loci (QTLs) related to resistance to disease listed in the literature |
| Disciplines: | Agrociencias, Biología, Ciencias de la computación |
| Keyword: | Plantas para uso industrial, Genética, Inteligencia artificial, Seleccion genómica, Hongos, Fitopatología, Café, Coffea arabica, Hemileia vastatrix, Algoritmos, Mejoramiento genético |
| Keyword: | Plants for industrial use, Genetics, Artificial intelligence, Genomic selection, Fungi, Phytopathology, Coffee, Coffea arabica, Hemileia vastatrix, Algorithms, Plant breeding |
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