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Replication Data for: Investigating genomic prediction strategies for grain carotenoid traits in a tropical/subtropical maize panel

CIMMYT Research Data & Software Repository Network Dataverse OAI Archive

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Title Replication Data for: Investigating genomic prediction strategies for grain carotenoid traits in a tropical/subtropical maize panel
 
Identifier https://hdl.handle.net/11529/10549016
 
Creator LaPorte, Mary-Francis
Suwarno, Willy Bayuardi
Hannok, Pattama
Koide, Akiyoshi
Bradbury, Peter
Crossa, Jose
Palacios-Rojas, Natalia
Diepenbrock, Christine Helen
 
Publisher CIMMYT Research Data & Software Repository Network
 
Description Vitamin A deficiency continues to cause challenges around the world including in areas where maize is an important component of human diets. Biofortification offers one solution for alleviating this deficiency. A Carotenoid Association Mapping panel, developed by the International Maize and Wheat Improvement Center (CIMMYT) contains 380 inbred lines adapted to tropical and subtropical environments that have varying grain concentrations of provitamin A and other health-beneficial carotenoids. The data in this study were used to assess the accuracy of several genomic prediction (GP) strategies for maize grain carotenoid traits within and between four environments in Mexico. Results are provided for these strategies including Ridge Regression-Best Linear Unbiased Prediction (including all markers versus subsets of markers), Elastic Net, Reproducing Kernel Hilbert Spaces, and Least Absolute Shrinkage and Selection Operator. The findings described in the accompanying journal article indicate the utility of genomic prediction methods for grain carotenoid traits and could inform their resource-efficient implementation in biofortification breeding programs.
 
Subject Agricultural Sciences
Zea mays
Plant Breeding
Agricultural research
Maize
Genomic Prediction
Provitamin A
Carotenoids
Biofortificaion
 
Language English
 
Date 2024
 
Contributor Dreher, Kate
U.S. Department of Energy
Genetic Resources Program (GRP)
Biometrics and Statistics Unit (BSU)
Global Maize Program (GMP)
CGIAR
 
Type Experimental data
Phenotypic data