So, I have a linear mixed model (using the function
lmer() in R) as follows:
lmer(set_metabolites ~ scale(total_metabolite_B) + scale(BMI) + scale(Age) + (1|Family)
Meaning, that I have a 1 metabolite (
metabolite_B) that I want to test against a set of metabolites (n = 300) for 1000 individuals. In the model I control for
Age, and my random effect is
This is done, and my results is a list 300 elements long, with beta values, SE and P-values. Now my question is, as I understand the multiple correction, in this case I have 300 independent test (right? one for each model). I now I need to correct for multiple testing, but I am pretty new with linear mixed models. Should I just take my list of 300 P-values and calculate the Bonferroni correction over those?