The two data sets were then independently subjected to permutation analyses, and statistical significance was reached between patients and controls for 24 analytes/ratios out of 137 in Platform 1 (p < 0.03) and for 31 analytes out of 156 in Platform 2 (p < 0.023), with an overlap of nine significant metabolites between both platforms (Table S4).
For example, there was a limited overlap of significant QTL within the Jersey breed.
Shutdown dissociation and PTSD symptom severity show a 67% overlap of significant correlations for unpleasant stimuli, which is considerable but does not suggest that the two measures are identical.
In contrast to pathway-based analysis showing significant overlap between the datasets, the gene-based approach revealed a low rate of overlap of significant genes (12.9% and 8.3%, respectively) under conditions of the same threshold p-values.
Lower pathway overlap numbers for the 10/10 case in the Wang/van de Vijver data sets gave a clearly less significant signal (an overlap of only 3 pathways), presumably because of the higher variability involving both subjects and measurement protocols.
We compared their genes with our data set from the same time point and found a 79% overlap of their significant genes with our data.
When we compared the 1,135 TR4 targets present within 1 kb of a TSS with 1,715 ELK4 targets found within 1 kb of a TSS, a significant overlap of 30% was observed.
We considered a feature (e.g., exon, intron) to be significantly bound with a given protein if greater than 10% of its length was overlapped by a region of significant enrichment for that mark.
Based on the observations above, we performed an overlap analysis of aging and behavior significant genes.
Combining all four datasets separately resulted in an significant overlap of 48 loci (p = 2.25 × 10-202) thavehave been consistently identified as targets for FOXP3 binding.
