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Replication Data for: Bayesian multi-trait kernel methods for 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 for multi-environment genome based prediction
 
Identifier https://hdl.handle.net/11529/10548565
 
Creator Montesinos-López, Osval A.
Montesinos-López, José Cricelio
Ramírez-Alcaraz, Juan Manuel
Poland, Jesse
Singh, Ravi
Dreisigacker, Susanne
Crespo Herrera, Leonardo Abdiel
Mondal, Suchismita
Govindan, Velu
Juliana, Philomin
Huerta Espino, Julio
Shrestha, Sandesh
Varshney, Rajeev K.
Montesinos-López, Abelardo
Crossa, Jose
 
Publisher CIMMYT Research Data & Software Repository Network
 
Description In breeding, multi-trait data can be used with different models for genomic prediction analyses. The data files associated with this dataset were used to explore Bayesian multi-trait kernel methods for genomic prediction and to compare the performance of the different analyses.
 
Subject Agricultural Sciences
Triticum aestivum
Agricultural research
Wheat
Heading time
Maturity time
Plant height
Grain yield
 
Language English
 
Date 2021
 
Contributor Dreher, Kate
United States Agency for International Development (USAID)
Bill and Melinda Gates Foundation (BMGF)
Foreign, Commonwealth and Development Office (FCDO)
Foundation for Research Levy on Agricultural Products (FFL)
Agricultural Agreement Research Fund (JA)
Genetic Resources Program (GRP)
Biometrics and Statistics Unit (BSU)
Global Wheat Program (GWP)
CGIAR Research Program on Wheat (WHEAT)
CGIAR Research Program on Maize (MAIZE)
Accelerating Genetic Gains in Maize and Wheat for Improved Livelihoods (AG2MW)
Stress Tolerant Maize for Africa
CGIAR
 
Type Phenotypic data
Genotypic data
Experimental data