I have 10,000 SNPs and 48 different phenotypic traits, so I performed GWAS based on Mixed Linear Model (MLM) approach using Tassel. I have got associations and now I am interested to see the distribution of p values to better interpret p-values so I extracted the p-values for all traits from the association file to generate a matrix containing 10473 rows (row one contain header) and 49 columns (column one shows marker names and rest 48 shows p-values of different traits), sample input file is:

```
Marker BM_day_HS1718 BM_day_HS1819 BM_HS1718
AX-94381147 0.30253 0.53712 0.37212
AX-94381170 0.00997 0.17313 0.00127
AX-94381338 0.21632 0.85283 0.26634
AX-94381448 0.65978 0.55148 0.78321
```

AX-94381449 0.34668 0.38499 0.67861

I have tried to do an extensive literature search, there are methods available to see the distribution of a single trait but I am interested to see for multiple traits, I found a method from scmamp library which is very close to my requirment but it first makes use of friedmanPost function to computes the raw p-values for the post hoc based on Friedman's test instead of plotting p-values I have in my matrix.

I have found another thread "How to represent multiple p-values" where they have recommended plotting effect size instead of p-values but my scenario is a bit different as I want to see the p-value distribution in order to decide the significance cutoff for GWAS results.

I have also tried to generate qq plot for multiple traits but it compared the expected vs actual p-values, I am not sure if it's good enough.

Any help/suggestion would be highly appreciated

why do you think a qq plot is not good enough? You can try a manhattan plot as well.

I have ploted the qq plot for all traits together but it depeicts expected vs actual p-values and the graph is not very much clear that's why i am trying to look for other options

For your case, I believe that the Manhattan plot will fit exactly what you need!

I have manhattan plots for each trait but is there any way by which I can plot manhattan plot for all traits together to see the p-value distribution

From the sound of it, you seems to be doing a phewas study. This package might be helpful https://pubmed.ncbi.nlm.nih.gov/24733291/