Hope this finds you well.
I am adding a covariance which is a quantitative variable (~600 samples) in my linear regression when I run a GWAS. The data turn out not normally distributed and I have tried many different ways to normalize it and outliers have been removed as well. Although the histograms and QQ plots of a few that were treated with cube root, Tukey ladder, box cox transformation looked fine. The normality tests still suggested that there are not normally distributed.
So my questions, is it ok to use one of them (after the normalizing) as a cavariance even it is not normally distributed (but close to)?
Thank you for your time!