Preliminary analysis was by ANOVA of each trait, with the marker effect nested within sire.
We did not observe a marker effect on the rate of evolutionary change.
We consider this in terms of marker effect size, which we can estimate in practical studies.
In this paper, the weights were derived from the marker effect estimation step which increases the computational burden.
The marker effect was explored via growth rate studies in isogenic Vibrio harveyi (Vh) strains altered in quorum sensing on the one hand, and bioluminescence on the other.
We detected no marker effect, indicating that fitness evolved similarly in ara− and ara+ microcosms (both ara− and ara+ populations: p = 0.0003; Table 1).
However, the marker effect estimation step might be not necessary as marker weights could be provided by existing genome-wide association studies (GWAS) or known candidate-gene effects.
To test for a marker effect, we performed a planned comparison between the ancestor and the derived population for each marker individually.
Assuming LD (D') between the QTL and the marker ranges from 0.1 to 1.0, we can calculate the marker effect size (see Appendix S1 for detail).
Figure 2 shows how the measured marker effect sizes are influenced by frequencies of the QTL and marker and LD between them.
The trait-specific relationship matrix TA is related to the trait of interest by including the information of both marker genotypes and the marker effect variances.
