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The Holstein and Jersey phenotypes were split into reference and validation datasets for each trait.
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The dataset contained, for each trait, 5 independent values per strain, across 37 strains.
A core dataset was created for each trait (Table 1) by requiring at least 50 observations per flock for each dam age class and birth-rearing type.
For each trait dataset the R [ 18] package GOseq [ 19], without the correction for gene length bias, was used to identify the KEGG pathways which were significantly over-represented (p < 0.05) by the set of genes compared against the background set of human genes.
ARVC was selected because it was found to be highly significant in the distances 0 and 20 kb datasets for the trait glucose (hypergeometric-empirical P values of 0.0004 and 0.0006, respectively).
Overlapping QTL regions between the two pig datasets was limited to one QTL for each trait.
Descriptive statistics for each trait and dataset are in Table 1.
Figures 1 and 2 present the results of the log-likelihood ratio test for each trait and dataset for the Gaussian, K- and C-kernels.
The empirical null distributions of the log-likelihood ratio test for each trait and dataset allowed the verification of the test assumptions and the control of type I errors based on the observed 95%% quartile.
Genomic predictions for each trait were estimated for the validation datasets using only animals in the prescribed reference dataset.
Minimum, mean and maximum estimates of genetic correlations between environments are given for each trait by breed and dataset.
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