Título
Replication Data for: Multi-generation genomic prediction of maize yield using parametric and non-parametric sparse selection indices
Autor
Marco Lopez-Cruz
Yoseph Beyene
Manje Gowda
Jose Crossa
Paulino Pérez-Rodríguez
Gustavo de los Campos
Nivel de Acceso
Acceso Abierto
Descripción
Abstracto - Genomic prediction models may be used in plant breeding pipelines. They are often calibrated using multi-generation data and there is an open question of whether all available data or a subset of it should be used to calibrate genomic prediction models. Therefore, a study was undertaken to determine whether combining sparse selection indexes (SSIs) and kernel methods could further improve prediction accuracy when training genomic models using multi-generation data. This dataset contains the genotypic and phenotypic data from CIMMYT maize doubled haploid lines that were used to perform the analyses. The results of the analyses are presented in the accompanying 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
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