Record Details

Replication Data for: Joint use of genome, pedigree and their interaction with environment for predicting the performance of wheat lines in new environments

CIMMYT Research Data & Software Repository Network Dataverse OAI Archive

View Archive Info
 
 
Field Value
 
Title Replication Data for: Joint use of genome, pedigree and their interaction with environment for predicting the performance of wheat lines in new environments
 
Identifier https://hdl.handle.net/11529/10548169
 
Creator Howard, Reka
Gianola, Daniel
Montesinos-López, Osval A.
Juliana, Philomin
Singh, Ravi
Poland, Jesse
Shrestha, Sandesh
Pérez-Rodríguez, Paulino
Crossa, Jose
Jarquín, Diego
 
Publisher CIMMYT Research Data & Software Repository Network
 
Description In this study, we evaluated genome-based prediction using 35,403 wheat lines from the Global Wheat Breeding Program of the International Maize and Wheat Improvement Center (CIMMYT). We implemented eight statistical models that included genome-wide molecular marker and pedigree information in two different validation schemes. All models included main effects, and others also considered interactions between the different types of covariates via Hadamard products of similarity structures. The pedigree models always gave better results predicting new lines in observed environments than the genome-based models when only main effects were fitted. However, for all traits, the highest predictive abilities were obtained when interactions between pedigree, markers and environments were included. When new lines were predicted in unobserved environments in almost all trait/year combinations, the marker main-effects model was the best. These results provide strong evidence that the different sources of genetic information (molecular markers and pedigree) are not equally useful at different stages of the breeding pipelines, and can be employed differentially to improve the design of future breeding programs.
 
Subject Agricultural Sciences
Genomic prediction
Days to maturity
Days to heading
Grain yield
Triticum aestivum
Agricultural research
Plant height
Lodging incidence
Wheat
 
Language English
 
Date 2019
 
Contributor Dreher, Kate
Genetic Resources Program (GRP)
Biometrics and Statistics Unit (BSU)
Global Wheat Program (GWP)
CGIAR Research Program on Wheat (WHEAT)
CGIAR
Foundation for Research Levy on Agricultural Products (FFL)
Agricultural Agreement Research Fund (JA)
Delivering Genetic Gain in Wheat (DGGW)
Bill and Melinda Gates Foundation (BMGF)
Department for International Development (DFID)
United States Agency for International Development (USAID)
Cornell University
Feed the Future Innovation Lab for Applied Wheat Genomics
 
Type Experimental data
Phenotypic data
Genotypic data