Very minor issue, but it looks like you're plotting ALL of your associations in your manhattan plots, rather than just the genotype associations. To clarify: when you run your GWAS, you include the top PCs as covariates in the regression (this is correct). But this means that you also get regression results for the covariates, not just the variants you're testing. Take a look at the TEST column in the .assoc.linear output file(s) of the plink --linear command to figure out which results you want to keep/plot.
Missing the code to make the boxplot!
Pretty plots
3/4
Exercise
Points Possible
Grade
Step 1.2 PC plot
1
1
Step 2.2 AFS plot
1
1
Step 3.2 Manhattan plots
1
1
Step 3.3 effect size boxplot
1
0
Grade
Total: 7.75/10
You're almost there! Other than addressing the minor issue with the manhattan plot, we need your code for the effect size boxplot and the boxplot itself! Feel free to keep working on this and resubmit!
README.md
with commands and analyses2/2
plotting.py
script to produce plots2.75/4
Very minor issue, but it looks like you're plotting ALL of your associations in your manhattan plots, rather than just the genotype associations. To clarify: when you run your GWAS, you include the top PCs as covariates in the regression (this is correct). But this means that you also get regression results for the covariates, not just the variants you're testing. Take a look at the
TEST
column in the.assoc.linear
output file(s) of theplink --linear
command to figure out which results you want to keep/plot.Missing the code to make the boxplot!
Pretty plots
3/4
Grade
Total: 7.75/10
You're almost there! Other than addressing the minor issue with the manhattan plot, we need your code for the effect size boxplot and the boxplot itself! Feel free to keep working on this and resubmit!