Título
Replication Data for: Sparse kernel models provide optimization of training set design for genome-based prediction in multi-year wheat breeding data
Autor
Marco Lopez-Cruz
Susanne Dreisigacker
Leonardo Abdiel Crespo Herrera
Alison Bentley
Ravi Singh
Suchismita Mondal
Paulino Pérez-Rodríguez
Jose Crossa
Nivel de Acceso
Acceso Abierto
Descripción
Abstracto - When genomic selection (GS) is used in breeding schemes, data from multiple generations can provide opportunities to increase sample size and thus the likelihood of extracting useful information from the training data. The Sparse Selection Index (SSI), is is a method for optimizing training data selection. The data files provided with this study include a large multigeneration wheat dataset of grain yield for 68,836 lines generated across eight cycles (years) as well as genotypic data that were analyzed to test this method. The results of the analysis are published in the corresponding journal article.
Editor
International Maize and Wheat Improvement Center
Fecha de publicación
2021
Tipo de recurso
Dataset
Recurso de información
Repositorio Orígen
Repositorio Institucional de Datos y Software de Investigación del CIMMYT
Descargas
0