[Note: crossposted on Biostars here]
My question is about how to know when the optimal statistical model has been selected for a GWAS (genome wide association study).
I appreciate that statistical models always provide only an approximation to a true process, and we aim to make inferences about those processes by choosing an appropriate model pragmatically (e.g. based on fit, complexity etc - there are no hard and fast rules).
But for a GWAS where there are many thousands or millions of tests being performed in parallel, how should we determine that one model is more appropriate than another?
A simple example: if we want to control for age, is it more appropriate to include a covariate for age only, or for age + age2, as I have seen done? A trickier example: I have a quantitative trait with excess zeroes (it is based on % response to treatment). It is better to use a 2-part model, a truncated model, some non-parametric test or just to press on regardless with a linear model?
To answer these questions using the data, is it sufficient simply to look at QQ plots and Genomic Control (or LDSC intercept)? I have mainly seen these methods used as a check for population structure in the past, but are they also appropriate for model selection? Or are there better approaches?