Table 1 QTL detection results with the different detection models for the different traits
Without SCAWith SCA
TraitModelNbR2popR2pop+QTLR2QTLR2*QTLR2pop+QTLR2QTLR2*QTL
DMCFounder alleles10 (4)32.460.127.640.963.832.447.9
SNP within-group12 (2)32.458.325.537.758.926.138.6
Hybrid genotype14 (1)32.458.625.637.959.026.238.8
DMYFounder alleles12 (5)21.949.527.735.555.134.243.9
SNP within-group9 (0)21.942.720.326.042.820.526.3
Hybrid genotype11 (3)21.942.019.725.243.220.926.8
DtSILKFounder alleles9 (2)15.046.631.436.951.536.743.2
SNP within-group15 (0)15.053.137.343.953.337.644.3
Hybrid genotype16 (3)15.049.934.140.251.335.641.9
PHFounder alleles11 (2)33.860.026.640.263.030.746.4
SNP within-group15 (4)33.858.724.737.360.326.640.2
Hybrid genotype13 (2)33.854.620.430.855.221.232.0
TotalFounder alleles42 (13)25.854.128.338.458.433.545.3
SNP within-group51 (6)25.853.226.936.253.827.737.4
Hybrid genotype54 (9)25.851.324.933.552.226.034.9
  • For each method and trait, we indicate the number of QTL detected (Nb) 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 dominance/SCA effects in the model). The percentage of variance explained by the population effect is also indicated (R2pop). The total number of detected QTL and the average percentages of variance explained over the different traits are also shown. Nb, number of QTL detected; SCA, Specific Combining Ability; DMC, dry matter content; DMY, dry matter yield; DtSILK, female flowering time; PH, plant height; QTL, quantitative trait loci.