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Replication Data for: Multi-generation genomic prediction of maize yield using parametric and non-parametric sparse selection indices

CIMMYT Research Data & Software Repository Network Dataverse OAI Archive

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Title Replication Data for: Multi-generation genomic prediction of maize yield using parametric and non-parametric sparse selection indices
 
Identifier https://hdl.handle.net/11529/10548608
 
Creator Lopez-Cruz, Marco
Beyene, Yoseph
Gowda, Manje
Crossa, Jose
Pérez-Rodríguez, Paulino
de los Campos, Gustavo
 
Publisher CIMMYT Research Data & Software Repository Network
 
Description Genomic prediction models may be used in plant breeding pipelines. They are often calibrated using multi-generation data and there is an open question of whether all available data or a subset of it should be used to calibrate genomic prediction models. Therefore, a study was undertaken to determine whether combining sparse selection indexes (SSIs) and kernel methods could further improve prediction accuracy when training genomic models using multi-generation data. This dataset contains the genotypic and phenotypic data from CIMMYT maize doubled haploid lines that were used to perform the analyses. The results of the analyses are presented in the accompanying article.
 
Subject Agricultural Sciences
Maize
Agricultural research
Plant Breeding
Zea mays
Plant height
Anthesis time
Grain yield
genotypes
KBLUP
GBLUP
GSSI
 
Language English
 
Date 2021-08-08
 
Contributor Dreher, Kate
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)
United States Agency for International Development (USAID)
CGIAR Research Program on Maize (MAIZE)
Accelerating Genetic Gains in Maize and Wheat for Improved Livelihoods (AGG)
Global Maize Program (GMP)
Genetic Resources Program (GRP)
Biometrics and Statistics Unit (BSU)
CGIAR
 
Type Dataset