The strongest mean intra-disorder correlation observed was for the genes causing Complement Component Deficiency, Ehlers-Danlos Syndrome and myopathy which all have mean intra-disorder correlation of over 0.26, which are highly significant and also are consistent with high expression in perhaps a restricted tissue or cell type.
Finally, we stress that although simulated data can produce a correlation between FST and α for low FST loci, this does not mean that the empirical correlations observed here for loci with low FST are not real, but rather that they should be interpreted with caution.
In part, however, this picture contrasts with the patterns of quasi-genetic correlations observed between the means of individual selection lines (rather than between the two treatment means).
More importantly, however, we found that the correlation between replicate measurements of the same protein sample were substantially better than the correlations observed among the individuals (mean R = 0.48, range 0.38 0.60, p<0.0005) (Figure S5).
Slight differences in mean correlation coefficients (0.012) were observed between the two breeds.
Overall performance is calculated as the mean correlation of predicted and observed expression profiles over all conditions, in all target clusters, normalized to the number of genes.
The significant correlations observed between rainfall in the year of birth and haplogroups {2,2,3} and {7,5,7} (Table 1) means that we cannot rule out selection after conception.
The mean correlation coefficient between predicted and observed site-specific amino acid distributions is larger than < r> = 0.70 for a mutation model with no free parameters and no genetic code.
The main insight from the mean correlation matrix is that we observe strong correlations of regulatory parameters with promoter thresholds.
Interestingly, comparing differential entropy to changes in the local mean absolute correlation, we observed that the absolute correlation was a good surrogate for entropy.
