Table 1. Genetic parameters of core subsets selected by different sampling methods at 16% sample size: advanced stochastic local search (ASLS), maximizing (M), maximum length sub-tree (MLST) and random (R).
| Subset Code | Method/allocation strategy | Cv (%) | DCE (±SD) | He | Sh | # Trait classes (%) | # haplotypes |
| OWGB Marrakech | 279 | 0.746 (±0.092) | 0.728 | 4.524 | 213 | 12 | |
| CC1-80 | ASLS/Cv1 | 279 (100) | 0.793 (±0.076) | 0.77 | 4.731 | 206 (96.7) | 12 (100) |
| CC2-80 | ASLS/DCE 1 | 234 (84) | 0.833 (±0.07) | 0.808 * | 4.829 | 202 (94.8) | 11 (91.6) |
| CC3-80 | ASLS/He 1 | 232 (83) | 0.828 (±0.067) | 0.814 * | 4.839 | 201 (94.3) | 11 (91.6) |
| CC4-80 | ASLS/Sh 1 | 250 (89.6) | 0.825 (±0.068) | 0.807 * | 4.861 | 204 (95.7) | 11 (91.6) |
| CC5-80 | ASLS/multi 2 | 265 (95) | 0.82 (±0.069) | 0.799 * | 4.836 | 205 (96.2) | 11 (91.6) |
| CC6-80 | ASLS/DCECv3 | 279 (100) | 0.806 (±0.071) | 0.779 | 4.773 | 205 (96.2) | 11 (91.6) |
| CC7-80 | M | 279 (100) | 0.804 (±0.07) | 0.786 | 4.773 | 204 (95.77) | 12 (100) |
| CC8-80 | MLST | 236 (84.6) | 0.817 (±0.061) | 0.797* | 4.778 | 205 (96.2) | 10 (83.3) |
| CC9-80 | R | 202 (72.4)* | 0.749 (±0.097) | 0.731 | 4.507 | 199 (93.4) | 10 (83.3) |
Four sampling strategies using the ASLS method were found to be the most suitable for comparing different sampling sizes (in bold).
Cv: allelic coverage or number of alleles, DCE: average genetic distance of Cavalli-Sforza and Edwards, SD: standard deviation, He: Nei diversity index, Sh: Shannon-Weaver diversity index.
Each parameter was optimized independently by performing 20 runs with 100% weight given to the respective parameters (“Cv strategy”, “DCE”, “Sh”, and “He”).
Twenty independent runs were performed with equal weight given to each of the four parameters simultaneously (“multi strategy”).
Subset sampled when a weight of 60% was assigned to DCE and 40% to Cv (“DCECv strategy”).
Statistically significant difference (p<0.05) using the Mann-Whitney test between core subsets and OWGB Marrakech.