Título
Genomic-enabled prediction based on molecular markers and pedigree using the Bayesian linear regression package in R
Autor
Paulino Pérez-Rodríguez
Gustavo de los Campos
Jose Crossa
Nivel de Acceso
Acceso Abierto
Resumen o descripción
The availability of dense molecular markers has made possible the use of genomic selection in plant and animal breeding. However, models for genomic selection pose several computational and statistical challenges and require specialized computer programs, not always available to the end user and not implemented in standard statistical software yet. The R-package BLR (Bayesian Linear Regression) implements several statistical procedures (e.g., Bayesian Ridge Regression, Bayesian LASSO) in a unifi ed framework that allows including marker genotypes and pedigree data jointly. This article describes the classes of models implemented in the BLR package and illustrates their use through examples. Some challenges faced when applying genomic-enabled selection, such as model choice, evaluation of predictive ability through cross-validation, and choice of hyperparameters, are also addressed.
Fecha de publicación
2010
Tipo de publicación
Artículo
Recurso de información
Formato
application/pdf
Idioma
Inglés
Audiencia
Investigadores
Repositorio Orígen
Repositorio Institucional de Publicaciones Multimedia del CIMMYT
Descargas
0