Table 2 Deployment strategies for genetic and genotypic gain calculations.
Model | Deployment Strategy | Description | Genetic Gain | Genotypic Gain |
|---|---|---|---|---|
True BV | Baseline (Optimal) | Selection based on the true BV of individuals ranked by their true BV under a given selection intensity (SI%) | \(\frac{\mu +\bar{B{V}_{{True},{SI} \% }}}{\mu }-1;\) where μ is the overall mean and \(\bar{B{V}_{{True},{SI} \% }}\) is the mean true breeding value of the top individuals selected based on their true BV at the given selection intensity | \(\frac{\mu +\bar{G{V}_{{True},{SI} \% }}}{\mu }-1;\) where μ is the overall mean and \(\bar{G{V}_{{True},{SI} \% }}\) is the mean true genotypic value of the top individuals selected based on their true GV |
Half-Sib (HS) | Pedigree-based selection | Individuals are ranked using BV predicted by the HS model, assuming a half-sibling relationship. Genetic gain is calculated using the true BV of selected individuals | \(\frac{\mu +\bar{B{V}_{{Model},{SI} \% }}}{\mu }-1;\) where \(\bar{B{V}_{{Model},{SI} \% }}\) is the mean true breeding value of individuals ranked using BV estimated by the HS Model | \(\frac{\mu +\bar{G{V}_{{Model},{SI} \% }}}{\mu }-1;\) where \(\bar{G{V}_{{Model},{SI} \% }}\) is the mean true genotypic value of individuals ranked using BV estimated by the HS Model |
GBLUP | Genomic-based selection | Individuals are ranked using BV predicted from the GBLUP model (additive genetic effects only). Genetic gain is calculated using the true BV of selected individuals | \(\frac{\mu +\bar{B{V}_{{Model},{SI} \% }}}{\mu }-1;\) where \(\bar{B{V}_{{Model},{SI} \% }}\) is the mean true breeding value of individuals ranked using BV estimated by the GBLUP Model | \(\frac{\mu +\bar{G{V}_{{Model},{SI} \% }}}{\mu }-1;\) where \(\bar{G{V}_{{Model},{SI} \% }}\) is the mean true genotypic value of individuals ranked using GV estimated by the GBLUP Model |
GDBLUP | Genomic selection with dominance | Individuals are ranked using BV or GV predicted from the GDBLUP model (additive + dominance effects). Gain is calculated using the true BV or GV of selected individuals | \(\frac{\mu +\bar{B{V}_{{Model},{SI} \% }}}{\mu }-1;\) where \(\bar{B{V}_{{Model},{SI} \% }}\) is the mean true breeding value of individuals ranked using BV estimated by the GDBLUP Model | \(\frac{\mu +\bar{G{V}_{{Model},{SI} \% }}}{\mu }-1;\) where \(\bar{G{V}_{{Model},{SI} \% }}\) is the mean true genotypic value of individuals ranked using GV estimated by the GDBLUP Model |
ssGDBLUP | Hybrid genomic selection | Individuals are ranked using BV or GV predicted from the ssGDBLUP model, which combines pedigree and genomic information with dominance effects | \(\frac{\mu +\bar{B{V}_{{Model},{SI} \% }}}{\mu }-1;\) where \(\bar{B{V}_{{Model},{SI} \% }}\) is the mean true breeding value of individuals ranked using BV estimated by the ssGDBLUP Model | \(\frac{\mu +\bar{G{V}_{{Model},{SI} \% }}}{\mu }-1;\) where \(\bar{G{V}_{{Model},{SI} \% }}\) is the mean true genotypic value of individuals ranked using GV estimated by the ssGDBLUP Model |
