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Combined Multistage Linear Genomic Selection Indices to Predict the Net Genetic Merit in Plant Breeding

CIMMYT Research Data & Software Repository Network Dataverse OAI Archive

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Title Combined Multistage Linear Genomic Selection Indices to Predict the Net Genetic Merit in Plant Breeding
 
Identifier https://hdl.handle.net/11529/10548356
 
Creator Cerón-Rojas, J. Jesus
Crossa, Jose
 
Publisher CIMMYT Research Data & Software Repository Network
 
Description Multistage selection is a cost-saving strategy for improving several traits because it is not necessary to measure all traits at each stage. A combined linear genomic selection index is a linear combination of phenotypic and genomic estimated breeding values useful to predict the individual net genetic merit, which in turn is a linear combination of the true unobservable breeding values of the traits weighted by their respective economic values. The main combined multistage linear genomic selection indices are the optimum and decorrelated indices. Using real and simulated data, we compared the efficiency of both indices to predict the net genetic merit in plants in a two-stage breeding context. The criteria used to compare the efficiency of both indices were that the total selection response of each index must be lower than or equal to the single-stage combined linear genomic selection index response and that the correlation of each index with the net genetic merit should be maximum. Using four different total proportions for the real data set, the total decorrelated and optimum index selection responses explained 90% and 97.5%, respectively, of the estimated single-stage combined selection response. In addition, at stage two, the correlation of the optimum and decorrelated indices with the net genetic merit were 0.84 and 0.63, respectively.
 
Subject Agricultural Sciences
Agricultural research
Genomic estimated breeding value
Molecular marker effects
Total selection response
 
Language English
 
Date 2019-12-06
 
Contributor Shrestha, Rosemary
Genetic Resources Program (GRP)
Biometrics and Statistics Unit (BSU)
Bill and Melinda Gates Foundation (BMGF)
United States Agency for International Development (USAID)
CGIAR Research Program on Wheat (WHEAT)
CGIAR Research Program on Maize (MAIZE)
CGIAR
 
Type Dataset