Genomic Selection—Considerations for Successful Implementation in Wheat Breeding Programs

In order to meet the goal of doubling wheat yield by 2050, breeders must work to improve breeding program efficiency while also implementing new and improved technologies in order to increase genetic gain. Genomic selection (GS) is an expansion of marker assisted selection which uses a statistical m...

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Bibliographic Details
Published inAgronomy (Basel) Vol. 9; no. 9; p. 479
Main Authors Larkin, Dylan Lee, Lozada, Dennis Nicuh, Mason, Richard Esten
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 23.08.2019
Subjects
Accuracy
Blight
climate change
Crop yield
Disease resistance
Fusarium head blight
genetic gain
Genomes
genomewide association studies (GWAS)
genomic estimated breeding values (GEBV)
genomic selection (GS)
Genomics
Germplasm
Grain
Heritability
Linkage disequilibrium
Marker-assisted selection
Markers
Mathematical models
Model accuracy
Optimization
Plant breeding
Polymorphism
Population
Population structure
Prediction models
Statistical models
Training
Wheat
wheat breeding
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Summary:In order to meet the goal of doubling wheat yield by 2050, breeders must work to improve breeding program efficiency while also implementing new and improved technologies in order to increase genetic gain. Genomic selection (GS) is an expansion of marker assisted selection which uses a statistical model to estimate all marker effects for an individual simultaneously to determine a genome estimated breeding value (GEBV). Breeders are thus able to select for performance based on GEBVs in the absence of phenotypic data. In wheat, genomic selection has been successfully implemented for a number of key traits including grain yield, grain quality and quantitative disease resistance, such as that for Fusarium head blight. For this review, we focused on the ways to modify genomic selection to maximize prediction accuracy, including prediction model selection, marker density, trait heritability, linkage disequilibrium, the relationship between training and validation sets, population structure, and training set optimization methods. Altogether, the effects of these different factors on the accuracy of predictions should be thoroughly considered for the successful implementation of GS strategies in wheat breeding programs.
ISSN:2073-4395
2073-4395
DOI:10.3390/agronomy9090479