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Replication Data for: Genome-based prediction of multiple wheat quality traits in multiple years

CIMMYT Research Data & Software Repository Network Dataverse OAI Archive

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Title Replication Data for: Genome-based prediction of multiple wheat quality traits in multiple years
 
Identifier https://hdl.handle.net/11529/10548423
 
Creator Ibba, Maria Itria
Crossa, Jose
Montesinos-López, Osval A.
Montesinos-López, Abelardo
Juliana, Philomin
Guzman, Carlos
Dolorean, Emily
Dreisigacker, Susanne
Poland, Jesse
 
Publisher CIMMYT Research Data & Software Repository Network
 
Description The use of genomic prediction could greatly help to increase the efficiency of selecting for wheat quality traits by reducing the cost and time required for this analysis. This study contains data used to evaluate the prediction performances of 13 wheat quality traits under two multi-trait models [Bayesian multi-trait multi-environment (BMTME) and multi-trait ridge regression (MTR)]. Separate files are provided for each year of data. An additional supplemental data file provides R code for running the analyses as well as a table describing the Average Pearson´s correlation (APC) and mean arctangent absolute percentage error (MAAPE) for the testing sets for each dataset and trait.
 
Subject Agricultural Sciences
Triticum aestivum
Agricultural research
Wheat
Grain weight
Grain test weight
Grain protein content
Grain hardness
Flour protein content
Flour SDS sedimentation
Dough mixograph mixing time
Dough mixograph torque
Dough alveograph value W
Dough alveograph P/L
Bread loaf volume
Thousand Kernel Weight
 
Language English
 
Date 2020-05-11
 
Contributor Dreher, Kate
United States Agency for International Development (USAID)
Bill and Melinda Gates Foundation (BMGF)
Foundation for Research Levy on Agricultural Products (FFL)
Agricultural Agreement Research Fund (JA)
Kansas State University
Cornell University
Genetic Resources Program (GRP)
Biometrics and Statistics Unit (BSU)
Global Wheat Program (GWP)
CGIAR Research Program on Wheat (WHEAT)
CGIAR
 
Type Dataset