Table 4.
Number of QTLs, explained phenotypic variance, and prediction ability per trait and location
| Trait a | Location | Analysis type b | QTLs c | R 2 adj d | R 2 pred e |
|---|---|---|---|---|---|
| SHO | Halle | Across | 28 | 81.3 | 63.3 |
| Dundee | Across | 12 | 41.8 | 18.7 | |
| SEL | Halle | Across | 23 | 70.9 | 45.9 |
| Dundee | Across | 11 | 19.6 | 0.4 | |
| HEA | Halle | Across | 25 | 82.8 | 65.5 |
| Dundee | Across | 23 | 81.1 | 63.4 | |
| RIP | Halle | Across | 29 | 62.6 | 33.6 |
| Dundee | Across | 19 | 52.4 | 26.8 | |
| MAT | Halle | Across | 22 | 75.9 | 56.5 |
| Dundee | Across | 22 | 53.6 | 23.1 | |
| HEI | Halle | Across | 23 | 82.4 | 67.0 |
| Dundee | Across | 22 | 79.7 | 64.8 | |
| LOD | Halle | Across | 16 | 70.9 | 52.0 |
| TCK | Dundee | N0 | 15 | 44.5 | 17.5 |
a Trait abbreviations are given in Table 1. b Phenotypic data used for GWAS were analysed across N treatments or restricted to N0 (low N). c Number of robust QTLs with detection rate of peak marker ≥25. d Mean explained phenotypic variance in the training set across all cross-validation runs in %. e Mean prediction ability in the validation set across all cross-validation runs in %.