All p-values were corrected for the false-positives introduced by the multiple testing presented by many time points using Bonferroni correction and 10-3 waselecteded as the cut-off threshold for p-values.
Only cytokines that remained significant after multiple testing are presented.
Multiple comparisons were done between different components, however, these were not independent tests and therefore P values uncorrected for multiple testing are presented.
After correcting for multiple testing, the present study failed to demonstrate a significant effect of the five COMT SNPs on cognition in boys and girls at ages 8 and 15 years, providing little evidence that COMT variation can have an effect on cognitive abilities in childhood and adolescence.
Overall, multiple tests are presented in Table 7 and the P values are not corrected for multiple tests.
Nominal one-sided P values (without adjustment for multiple tests) are presented for analyses of primary and secondary effectiveness outcomes; 2-sided P values for safety outcomes.
The results were not corrected for multiple testing, since the present study is an exploratory study and each of the individual associations was considered to be of interest.
We used permutation to generate empirical p-values and control for multiple testing: power is presented correcting for all tests in a particular gene-by-gene comparison, and also at the experiment-wide level.
We will first state our problem formally, then provide the necessary background on statistical association testing and the multiple testing problem, before presenting our approach to genetic heterogeneity detection.
These results remained significant after adjustment for multiple testing and are presented in Table 4. None of the other interactions tested approached significance (see additional files 1 and 2).
