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Replication Data for: Sparse testing using genomic predication improves selection for breeding targets in elite spring wheat

CIMMYT Research Data & Software Repository Network Dataverse OAI Archive

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Title Replication Data for: Sparse testing using genomic predication improves selection for breeding targets in elite spring wheat
 
Identifier https://hdl.handle.net/11529/10548639
 
Creator Atanda, Sikiru Adeniyi
Govindan, Velu
Singh, Ravi
Robbins, Kelly R.
Crossa, Jose
Bentley, Alison R.
 
Publisher CIMMYT Research Data & Software Repository Network
 
Description In multi-environment yield trials, the use of sparse testing genomic selection enables increasing selection intensity or testing environments. The data presented in this dataset were used in the evaluation of different sparse testing genomic selection strategies in the early yield testing stage of CIMMYT spring wheat breeding pipeline. Phenotypic, genotypic, and coefficient of parentage data are provided. The germplasm is made up of multiple populations each with small family sizes. The findings of the study are detailed in an accompanying article.
 
Subject Agricultural Sciences
Wheat
Triticum aestivum
Agricultural research
Plant breeding
genotypes
Grain yield
Selection index
Genetic gain
Factor analytic
Prediction accuracy
Genomic best linear unbiased estimate
Genotype x environment interaction
 
Language English
 
Date 2022
 
Contributor Dreher, Kate
Foreign, Commonwealth and Development Office (FCDO)
Bill and Melinda Gates Foundation (BMGF)
Global Wheat Program (GWP)
Genetic Resources Program (GRP)
Biometrics and Statistics Unit (BSU)
CGIAR Research Program on Wheat (WHEAT)
CGIAR
 
Type Genotypic data
Phenotypic data
Experimental data