| Revista: | Scientia Agricola |
| Base de datos: | PERIÓDICA |
| Número de sistema: | 000455420 |
| ISSN: | 0103-9016 |
| Autores: | Costa, Jaquicele Aparecida da1 Azevedo, Camila Ferreira1 Nascimento, Moysés1 Silva, Fabyano Fonseca e2 Resende, Marcos Deon Vilela de3 Nascimento, Ana Carolina Campana1 |
| Instituciones: | 1Universidade Federal de Vicosa, Departamento de Estatistica, Vicosa, Minas Gerais. Brasil 2Universidade Federal de Vicosa, Departamento de Zootecnia, Vicosa, Minas Gerais. Brasil 3Universidade Federal de Vicosa, Departamento de Engenharia Florestal, Vicosa, Minas Gerais. Brasil |
| Año: | 2022 |
| Volumen: | 79 |
| Número: | 6 |
| País: | Brasil |
| Idioma: | Inglés |
| Tipo de documento: | Artículo |
| Enfoque: | Experimental, analítico |
| Resumen en inglés | The principal component regression (PCR) and the independent component regression (ICR) are dimensionality reduction methods and extremely important in genomic prediction. These methods require the choice of the number of components to be inserted into the model. For PCR, there are formal criteria; however, for ICR, the adopted criterion chooses the number of independent components (ICs) associated to greater accuracy and requires high computational time. In this study, seven criteria based on the number of principal components (PCs) and methods of variable selection to guide this choice in ICR are proposed and evaluated in simulated and real data. For both datasets, the most efficient criterion and that drastically reduced computational time determined that the number of ICs should be equal to the number of PCs to reach a higher accuracy value. In addition, the criteria did not recover the simulated heritability and generated biased genomic values |
| Disciplinas: | Agrociencias |
| Palabras clave: | Gramíneas, Fitotecnia, Arroz, Oryza sativa, Genoma, Mejoramiento genético |
| Keyword: | Crop husbandry, Gramineae, Rice, Oryza sativa, Genome, Plant breeding |
| Texto completo: | Texto completo (Ver HTML) Texto completo (Ver PDF) |
