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Replication Data for: Approximate kernels for large data sets In genome-based prediction

CIMMYT Research Data & Software Repository Network Dataverse OAI Archive

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Title Replication Data for: Approximate kernels for large data sets In genome-based prediction
 
Identifier https://hdl.handle.net/11529/10548425
 
Creator Cuevas, Jaime
Montesinos-López, Osval A.
Martini, Johannes
Pérez-Rodríguez, Paulino
Lillemo, Morten
Crossa, Jose
 
Publisher CIMMYT Research Data & Software Repository Network
 
Description The rapid development of molecular markers and sequencing technologies has made it possible to use genomic selection (GS) and genomic prediction (GP) in animal and plant breeding. However, computational difficulties arise when the number of observations is large. This five datasets provided here were used to support a comparative analysis of two genomic-enabled prediction models: the full genomic method single environment (FGSE) and the approximate kernel method for a single environment model (APSE). The data were also used to compare the full genomic method with genotype × environment model (FGGE) to the approximate kernel method with genotype × environment interaction (APGE). The results of the analyses are described in the related publication.
 
Subject Agricultural Sciences
Triticum aestivum
Genome-based prediction
Agricultural research
Wheat
 
Language English
 
Date 2020
 
Contributor Dreher, Kate
United States Agency for International Development (USAID)
Bill and Melinda Gates Foundation (BMGF)
Genetic Resources Program (GRP)
Biometrics and Statistics Unit (BSU)
Global Wheat Program (GWP)
CGIAR Research Program on Maize (MAIZE)
CGIAR Research Program on Wheat (WHEAT)
CGIAR
Foundation for Research Levy on Agricultural Products (FFL)
Agricultural Agreement Research Fund (JA)
Cornell University
Kansas State University
 
Type Phenotypic data
Genotypic data
Experimental data