Table 3.
Identification accuracies (Mean (SE) over 50 replications) when the reference population was genotyped with sequencing and the test population was genotyped with different SNP chips or with sequencinga
| Chip/SEQ | No. MBI SNPs containedb | Imputation accuracy | Identification accuracy, % | |||
|---|---|---|---|---|---|---|
| KNN | RF | SVM | KSR | |||
| 50K | 20 | 83.58% | 99.69 (0.01) | 97.87 (0.03) | 98.01 (0.00) | 99.16 (0.02) |
| 80K | 52 | 88.52% | 99.86 (0.00) | 99.19 (0.04) | 98.16 (0.00) | 99.51 (0.03) |
| 100K | 65 | 89.41% | 99.33 (0.01) | 98.75 (0.03) | 98.01 (0.00) | 98.84 (0.02) |
| 150K | 91 | 91.16% | 99.65 (0.01) | 99.04 (0.03) | 98.01 (0.00) | 99.19 (0.03) |
| 777K | 261 | 94.36% | 99.72 (0.00) | 99.15 (0.03) | 98.01 (0.00) | 99.30 (0.03) |
| SEQ | 2,000 | – | 99.86 (0.00) | 99.24 (0.03) | 98.16 (0.00) | 99.65 (0.03) |
aThe chip genotypes were imputed to sequence level. The reference population size was 30 individuals per breed and 2,000 most breed-informative SNPs derived by DFI were used
bNumber of SNPs among the 2,000 most breed-informative (MBI) SNPs derived from the reference population which were contained in the chips
KNN K-Nearest Neighbor, RF Random Forest, SVM Support Vector Machine, KSR, an integration of KNN, SVM and RF