I am using regression models to test the effect of two genetic mutations (let's say SNP1 and SNP2) on a cognitive error-evaluation measure (let's say CEEM), and I am controlling for age. I run the following models:
CEEM ~ SNP1 + Age
CEEM ~ SNP2 + Age
CEEM ~ SNP1 * Age
CEEM ~ SNP2 * Age
Here, there are significant main effects (F) of each SNP on CEEM as well as significant interactions between each SNP and Age on CEEM. The regression models are showing 'significant' effects.
Because my two genes contribute to a similar molecular mechanism, I combine them by a simple summation method, unweighted genetic risk score (uGRS), and run the models:
CEEM ~ uGRS + Age
CEEM ~ uGRS * Age
Here, the same story - significant main effect of uGRS and significant interaction.
In my first, single-gene models which include '+ Age', the slope (beta) is nonsignificant. So, my question is: complex interactions aside, to what extent does that matter? Can I report these beta-coefficients, their associated t-tests and significance levels without concern?