All gains were estimated using predicted breeding values of individual traits (i.e. RNC or NC) using spatial analyses.
Female breeding experience predicted breeding date and clutch size, but mate ornamentation and its variance components were unrelated to experience.
The gBLUP method predicted breeding values more accurately than BLUP.
Predicted breeding values and reliabilities based on genomic information were compared with those obtained from traditional breeding values.
The ranks of predicted phenotypes from GLMM and RMM.t were highly correlated: 0.97 for line W1 and 0.98 for line WB (Table 3), which means that there was almost no re-ranking by using GLMM or RMM.t to predict breeding values.
The simulated variance components were used to predict breeding values.
Various methods are used to predict breeding values from genomic data.
Langlois [ 1] used transformed ranks to predict breeding values for jumping horses.
These models have been successfully used to predict breeding values in plants and animals.
However, as the number of QTL increases this ability to predict breeding values decreases.
