Hi all.

Is it necessary, previous to conducting a GWAS (MLMM approach, Multi locus mixed model), to normalize quantitative phenotypes to make them follow a normal distribution?

After applying the Shapiro-Wilk test for each phenotype, I have observed that none of the four traits studied follow a normal distribution. See for example a histogram for a quantity of a compound "A".

I have performed GWAS with the phenotypes un-normalized and the QQ-plots obtained for each one of them are:

Except for the compound B (if I have to choose one), the plots seem ok to me. If a previous normalization is required, which one should I use? I have read about quantile normalization or the rank-based inverse normal transformation, which seems to be more popular. Thanks in advance.

Thanks for your help!

Although it seems that there are many zero values, actually those are zero-points (0.2, 0.5, 0.3, etc). There are only 3 zero values in the all set of phenotypes. Would then your approach be necessary? If yes, could you recommend me any tool to perform this analysis?