Genomic Selection—Considerations for Successful Implementation in Wheat Breeding Programs
Abstract
:1. Introduction
2. The Breeder’s Equation and Genetic Gain
3. Marker Assisted Selection
4. Impact of Genomic Selection on the Breeder’s Equation
5. Different Factors Affecting the Accuracy of Genomic Selection
5.1. Marker Density
5.2. Prediction Models
5.3. Training Population Size
5.4. Trait Heritability
5.5. Genetic Relationship between Training and Validation Sets
5.6. Population Structure
5.7. Retraining and Training Population Composition
6. Genomic Selection in Multiple Environments
7. Genomic Selection for Multiple Traits
8. Use of GWAS Results in Genomic Selection
9. Putting the Pieces Together: Genomic Selection for Wheat Grain Yield
10. Putting the Pieces Together: Genomic Selection for Fusarium Head Blight Resistance
11. Conclusions
Author Contributions
Funding
Conflicts of Interest
References
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Factor | Factor on Breeder’s Equation Impacted | Effect on Selection Accuracy (r) | Consideration for Breeding |
---|---|---|---|
Marker density | Selection accuracy | Varies | When LD is high a small number of markers are needed |
Choice of prediction model | Varies | Models vary in their ability to account for larger effect QTL and epistatic interactions | |
Training population size | Genetic variance | Increased r for increase in training population size | Greater diversity requires a larger training population size |
Trait heritability | Selection accuracy | Varies | In the absence of large effect QTL, accuracy generally increases with trait heritability |
Genetic relationship between training and validation | Selection accuracy | Increased r as more genetically related populations or families are used | Should use genetically related training and validation populations |
Population structure | Selection accuracy | Varies | Sub-populations can lead to erroneous predictions |
Training population composition | Selection accuracy | Varies | Should use a training population that captures genetic relationships with the validation population |
Genotype by environment interaction | Additive genetic variance | Increased r when GxE effects fitted in the model | GxE together with pedigree and marker information could help improve accuracy |
Multiple traits | Additive genetic variance | Increased r when multiple traits are fitted in the model | Accounts for additional genetic variance |
Use of GWAS results | Additive genetic variance | Increased r for incorporation of GWAS results in the model (for most empirical studies) | Accounts for large effect QTL |
Multiple generations per year | Breeding cycle time | Varies; r affected by one or a combination of factors above | Increases gain per time by reducing cycle time |
Early generation parent selection | Breeding cycle time, selection intensity | Varies; r affected by one or a combination of factors above | Increases gain per time by expediting the recombination of favorable alleles |
Selection intensity | Selection accuracy | Varies; r affected by one or a combination of factors above | Genomic selection allows more lines to be characterized and thus increased in selection intensity |
Additive genetic variance | Selection accuracy | Varies; r affected by one or a combination of factors above | High additive genetic variance for increased gains |
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Larkin, D.L.; Lozada, D.N.; Mason, R.E. Genomic Selection—Considerations for Successful Implementation in Wheat Breeding Programs. Agronomy 2019, 9, 479. https://doi.org/10.3390/agronomy9090479
Larkin DL, Lozada DN, Mason RE. Genomic Selection—Considerations for Successful Implementation in Wheat Breeding Programs. Agronomy. 2019; 9(9):479. https://doi.org/10.3390/agronomy9090479
Chicago/Turabian StyleLarkin, Dylan Lee, Dennis Nicuh Lozada, and Richard Esten Mason. 2019. "Genomic Selection—Considerations for Successful Implementation in Wheat Breeding Programs" Agronomy 9, no. 9: 479. https://doi.org/10.3390/agronomy9090479