Table 3. QTL detection results for the different traits.
| Without SCA | With SCA | |||||||
|---|---|---|---|---|---|---|---|---|
| Trait | No. | R2pop | R2pop+QTL | R2QTL | R2*QTL | R2pop+QTL | R2QTL | R2*QTL |
| DMC | 10 (4) | 32.4 | 60.1 | 27.6 | 40.9 | 63.8 | 32.4 | 47.9 |
| DMY | 12 (5) | 21.9 | 49.5 | 27.7 | 35.5 | 55.1 | 34.2 | 43.9 |
| DtSILK | 9 (2) | 15.0 | 46.6 | 31.4 | 36.9 | 51.5 | 36.7 | 43.2 |
| PH | 11(2) | 33.8 | 60.0 | 26.6 | 40.2 | 63.0 | 30.7 | 46.4 |
Shown are the QTL detection results for the different traits: DMC, DMY, female flowering time (DtSILK), and PH. For each trait we indicated the number of QTL detected (No.) and between brackets the number of these QTL showing significant SCA effects at a 5% individual risk level, the proportion of the phenotypic variance (R2QTL, in %), and of the within-population phenotypic variance (R2*QTL, in %) explained by the detected QTL (with and without including SCA effects in the model). The percentage of variance explained by the population effect is also indicated (R2pop).