A graph model for genomic prediction in the context of a linear mixed model framework
Genomic selection is revolutionizing both plant and animal breeding, with its practical application depending critically on high prediction accuracy. In this study, we aimed to enhance prediction accuracy by exploring the use of graph models within a linear mixed model framework. Our investigation r...
| Autores principales: | , , , , |
|---|---|
| Formato: | Journal Article |
| Lenguaje: | Inglés |
| Publicado: |
Wiley
2024
|
| Materias: | |
| Acceso en línea: | https://hdl.handle.net/10568/159671 |
Ejemplares similares: A graph model for genomic prediction in the context of a linear mixed model framework
- Refining penalized ridge regression: a novel method for optimizing the regularization parameter in genomic prediction
- Balancing sensitivity and specificity enhances top and bottom ranking in genomic prediction of cultivars
- Algorithmic differentiation of linear mixed models with variance-covariance structures
- Multilevel diagnostics for mixed and hierarchical linear models
- Using an incomplete block design to allocate lines to environments improves sparse genome-based prediction in plant breeding
- Linear Models for the Prediction of the Genetic Merit of Animals