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Replication Data for: Bayesian multi-trait kernel methods improve multi-environment genome based prediction

CIMMYT Research Data & Software Repository Network Dataverse OAI Archive

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Title Replication Data for: Bayesian multi-trait kernel methods improve multi-environment genome based prediction
 
Identifier https://hdl.handle.net/11529/10548629
 
Creator Montesinos-López, Osval A.
Montesinos-López, José Cricelio
Montesinos-López, Abelardo
Ramírez-Alcaraz, Juan Manuel
Poland, Jesse
Singh, Ravi
Dreisigacker, Susanne
Crespo Herrera, Leonardo Abdiel
Govindan, Velu
Juliana, Philomin
Huerta Espino, Julio
Shrestha, Sandesh
Varshney, Rajeev K.
Crossa, Jose
 
Publisher CIMMYT Research Data & Software Repository Network
 
Description When multi-trait data are available and the degree of correlation between phenotypic traits is moderate or large, genomic prediction accuracy can increase when models are used that account for correlations between the phenotypic traits. The data files associated with this dataset were used to explore Bayesian multi-trait kernel methods for genomic prediction. Linear, Gaussian, polynomial and sigmoid kernels were studied and compared with the GBLUP multi-trait models. The results of the analysis are reported in the accompanying article.
 
Subject Agricultural Sciences
Wheat
Triticum aestivum
Agricultural research
Plant Breeding
Plant height
Heading time
Maturity time
Grain yield
 
Language English
 
Date 2021
 
Contributor Dreher, Kate
Foreign, Commonwealth and Development Office (FCDO)
Foundation for Research Levy on Agricultural Products (FFL)
Accelerating Genetic Gains in Maize and Wheat for Improved Livelihoods (AGG)
Agricultural Agreement Research Fund (JA)
CGIAR
Biometrics and Statistics Unit (BSU)
CGIAR Research Program on Maize (MAIZE)
United States Agency for International Development (USAID)
Bill and Melinda Gates Foundation (BMGF)
Global Wheat Program (GWP)
Genetic Resources Program (GRP)
CGIAR Research Program on Wheat (WHEAT)
 
Type Genotypic data
Phenotypic data
Experimental data