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Replication Data for: Optimizing Genomic-Enabled Prediction: A Feature Weighting Approach for Enhancing within Family Accuracy

CIMMYT Research Data & Software Repository Network Dataverse OAI Archive

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Title Replication Data for: Optimizing Genomic-Enabled Prediction: A Feature Weighting Approach for Enhancing within Family Accuracy
 
Identifier https://hdl.handle.net/11529/10548950
 
Creator Montesinos-López, Osval A.
Crespo Herrera, Leonardo Abdiel
Xavier, Alencar
Godwa, Manje
Beyene, Yoseph
Saint Pierre, Carolina
De la Rosa-Santamaria, Roberto
Salinas-Ruiz, Josafhat
Gerard, Guillermo
Vitale, Paolo
Dreisigacker, Susanne
Lillemo, Morten
Grignola, Fernando
Sarinelli, Martin
Pozzo, Ezequiel
Quiroga, Marco
Montesinos-López, Abelardo
Crossa, Jose
 
Publisher CIMMYT Research Data & Software Repository Network
 
Description This study provides supplemental data to support the study on Optimizing Genomic-Enabled Prediction: A Feature Weighting Approach for Enhancing within Family Accuracy.
 
Subject Agricultural Sciences
Agricultural research
Maize
Sorghum
Zea mays
Genomic selection
Genomic best linear unbiased predictor
GBLUP
 
Language English
 
Date 2021
 
Contributor Shrestha, Rosemary
Bill and Melinda Gates Foundation (BMGF)
Accelerating Genetic Gains in Maize and Wheat for Improved Livelihoods (AG2MW)
United States Agency for International Development (USAID)
CGIAR Research Program on Wheat (WHEAT)
CGIAR Research Program on Maize (MAIZE)
Foundation for Research Levy on Agricultural Products (FFL)
Agricultural Agreement Research Fund (JA)
Global Maize Program (GMP)
Global Wheat Program (GWP)
Genetic Resources Program (GRP)
Biometrics and Statistics Unit (BSU)
 
Type Experimental data
Genotypic data
Phenotypic data