I started reading some papers on X-wide association studies, where X can be metabolome, epigenome, etc... The authors usually describe which are the dependent and which are the independent variables of their models. Taking as an example a metabolome-wide association study (MWAS), the authors use as model the following:
condition. That is, each metabolite is considered the dependent variable and the condition (e.g., smoker vs. non-smoker) is considered the independent variable. I was a little bit confused, so I looked into an example study using GWAS: in this case the model can be written as
SNPs, which is what I would expect (knowing nothing about GWAS but something about Data Science) and quite the opposite from the MWAS described above.
Does the choice of the dependent variable depend on the questions asked in the study, or does it exist some sort of agreement? Moreover, regarding the GWAS model described above, I read that usually researchers fit a model for each SNP: what's wrong with fitting a model which consists of several independent variables (i.e.,
y ~ SNP_1 + SNP_2 + ... + SNP_N)?