I am looking for some help on the following matter. I have to analyse the association between SNPs and depressive symptoms. Genetic predisposition of the SNP will be categorised in either 0,1 or 2 minor alleles (exposure variable). Depressive symptoms will be measured over time (T0,T1,T2,T3) and kept continuous between a score of 0-60. First I want to assess the association and afterwards I might want to adjust for e.g., age, sex, BMI.
My initial tought was a linear model, however than I will not measure change over time. So my second thought was mixed linear models. However, I encounter the problem that I will than look at differences between indivduals and not between the genetic predisposition. Is there a way I can use lme4 (or gglme if depressive symptoms is skewed) where I stratify for genetic predisposition or look directly at the effect of genetic predisposition on the depressive symptoms?
Another idea I had was just ignore the ID number of the participants and just analyse it as: lmer(depressive_symptoms~1 + (1|SNP), data=df) -> does this make sense? I get very weird plots if I do this.
I hope somebody can help me. (I do not yet have a script/database or outcome as I am writing my analysis plan for a proposal, so I cannot include any of that..)
**my question is: what model best to use? **
Thanks in advance,
I tried to look it up in books, websites etc. but I can only find between individuals.