# Performing multiple testing correction (Bonferroni / FDR) on p-value from lmer()

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 BMI and Age, and my random effect is Family.

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?