Due to the large number of tests, and the unsuitability of permutations as a correction for multiple testing in interaction analyses, the false discovery rate method was used, although no associations remained significant after correction.
Unlike earlier studies that did not account for potential false discoveries from random, multiple testing, the interaction term seen here for female sex and HLA-A*03 01 HLA-A*03 01nied by a lowasDR (<0.10).
A p value <0.05 was considered to indicate an important modifier for both multiplicative and additive interaction assessments, despite the multiple tests as interaction tests tend to be underpowered [ 22].
Our study did not replicate the interaction between GSTM1 and SE in spite of the sufficient power and multiple tests for interaction.
Third, potential modifications between the UPMS score and age, sex or any of the risk factors stated above were tested by adding the respective interaction term to the fully-adjusted model 5. To account for multiple testing in the interaction analyses, the significance level was corrected by the Bonferroni method leading to a significance level of 0.005 (as ten variables were under concern).
In addition, after adjusting for multiple testing, no significant interactions between any pairs of SNPs on incident T2DM were observed (data not shown).
After adjusting the p values for multiple testing, we found an interaction between BMI and height for total cancer in men (p < 0.001), but no interaction between height and smoking.
However, after applying the Bonferroni correction to adjust p-values for multiple testing, none of the interaction effects remained significant.
However, this could be expected by chance when doing multiple testing, and testing for interaction between smoking (yes/no) and coffee consumption gave no significance (P=0.08).
However, the Bonferroni correction was used to correct for multiple testing, and each pairwise interaction among the 10 subgroups was considered significant when Pinteraction was less than 0.005.
