Título
Replication Data for: Multi-trait genome prediction of new environments with partial least squares
Autor
Osval Antonio Montesinos-Lopez
Brandon Alejandro Mosqueda González
Marco Alberto Valenzo-Jimenez
Jose Crossa
Nivel de Acceso
Acceso Abierto
Descripción
Abstracto - The genomic selection (GS) methodology has revolutionized plant breeding. This methodology makes predictions for genotyped candidate lines based on statistical machine learning algorithms that are trained with phenotypic and genotypic data of a reference population. GS can save significant resources in the selection of candidate individuals. However, plant breeders can face challenges when trying to implement it practically to make predictions for future seasons or new locations and/or environments. To help address this challenge, this study seeks to explore the use of the multi-trait partial least square (MT-PLS) regression methodology and to compare its performance with the Bayesian Multi-trait Genomic Best Linear Unbiased Predictor (MT-GBLUP) method. A benchmarking process was performed with five actual data sets contained in this study. The results of the analysis are reported in the accompanying article.
Editor
International Maize and Wheat Improvement Center
Fecha de publicación
2022
Tipo de recurso
Dataset
Recurso de información
Repositorio Orígen
Repositorio Institucional de Datos y Software de Investigación del CIMMYT
Descargas
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