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Replication Data for: Sparse kernel models provide optimization of training set design for genome-based prediction in multi-year wheat breeding data

CIMMYT Research Data & Software Repository Network Dataverse OAI Archive

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Title Replication Data for: Sparse kernel models provide optimization of training set design for genome-based prediction in multi-year wheat breeding data
 
Identifier https://hdl.handle.net/11529/10548635
 
Creator Lopez-Cruz, Marco
Dreisigacker, Susanne
Crespo-Herrera, Leonardo
Bentley, Alison R.
Singh, Ravi
Mondal, Suchismita
Perez-Rodriguez, Paulino
Crossa, Jose
 
Publisher CIMMYT Research Data & Software Repository Network
 
Description When genomic selection (GS) is used in breeding schemes, data from multiple generations can provide opportunities to increase sample size and thus the likelihood of extracting useful information from the training data. The Sparse Selection Index (SSI), is is a method for optimizing training data selection. The data files provided with this study include a large multigeneration wheat dataset of grain yield for 68,836 lines generated across eight cycles (years) as well as genotypic data that were analyzed to test this method. The results of the analysis are published in the corresponding journal article.
 
Subject Agricultural Sciences
Wheat
Triticum aestivum
Agricultural research
Plant Breeding
Genotypes
 
Language English
 
Date 2021-12-03
 
Contributor Dreher, Kate
Foreign, Commonwealth and Development Office (FCDO)
Foundation for Research Levy on Agricultural Products (FFL)
USDA National Institute of Food and Agriculture
Accelerating Genetic Gains in Maize and Wheat for Improved Livelihoods (AGG)
Agricultural Agreement Research Fund (JA)
CGIAR
Biometrics and Statistics Unit (BSU)
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 Dataset