Table 4.
Modela | Trainingb | Accuracyc | SDd |
---|---|---|---|
Litter size | |||
GS | LW | 0.06 | 0.10 |
LR | 0.07 | 0.11 | |
F1 | 0.23 | 0.08 | |
BS | LW | 0.06 | 0.11 |
LR | 0.06 | 0.13 | |
LW and LR* | 0.09 | 0.12 | |
F1LW | 0.21 | 0.08 | |
F1LR | 0.12 | 0.09 | |
F1LW and F1LR* | 0.23 | 0.08 | |
Gestation length | |||
GS | LW | 0.42 | 0.08 |
LR | 0.30 | 0.09 | |
F1 | 0.52 | 0.08 | |
BS | LW | 0.39 | 0.08 |
LR | 0.23 | 0.10 | |
LW and LR* | 0.45 | 0.08 | |
F1LW | 0.43 | 0.08 | |
F1LR | 0.34 | 0.09 | |
F1LW and F1LR* | 0.53 | 0.08 |
* Predicted direct genomic value was the “total direct genomic value” (sum of the breed-specific direct genomic values)
aGS, traditional genomic selection model; BS, model that accounts for breed-specific effects
bLW, Large White; LR, Landrace; F1, two-way crossbred; F1LW, alleles of the F1 population inherited from the LW population; F1LR, alleles of the F1 population inherited from the LR population. Training populations in each replicate were defined as a random set of 90% (N = 832) of the animals used for the estimation of variance components
cAverage of the 40 replicates; accuracy was defined as the correlation between the direct genomic values of the validation population [random set of 10% (N = 92) of the crossbred animals used for the estimation of variance components] and their average pre-adjusted phenotypes in each replicate
dStandard deviation over replicates; the highest accuracies for each model and trait are indicated in bold