I'm working on a case/control study and including some other variables such as genotypes. One thing I want to do is a subgroup analysis of the genotypes for just the case group. The brute force method is to subset my data to the case group and run deseq on that. I'm very new to deseq and the very idea of contrasts. But from reading it seems like contrasts offer a more elegant way to do subgroup analysis without having to rerun deseq multiple times on differently subsetted data.

Suppose I have the following variables:

• Case: [0,1]
• Gene1: [YY, YN, NN]
• Gene2: [YY, YN, NN]

I run deseq with the design "~ Case + Gene1 + Gene2 + Gene1:Gene2"

How would I write contrasts that would be the equivalent of

1. Subgroup just those with the disease (Case=1)
2. Subgroup just homozygous genotypes: (Gene1 = [YY, NN], Gene2=[YY,NN])
3. Ask the question: Is the strongest effect due to Gene1, Gene2, or the interaction?

Thanks!

Here is a putative study design to work with:

> fake_study_design = data.frame(
+     Case=sample(c('Case', 'Control'), 10, replace=T), 
+     GeneA=sample(c('YY','YN','NN'), 10, replace=T), 
+     GeneB=sample(c('YY', 'YN', 'NN'), 10, replace=T)
+ )
> fake_study_design
      Case GeneA GeneB
1     Case    YY    YY
2     Case    YN    NN
3  Control    YY    NN
4  Control    YY    YN
5  Control    YN    YY
6  Control    NN    NN
7  Control    NN    YY
8     Case    NN    YN
9  Control    YY    NN
10 Control    YY    YY

I can't follow, can you show the sample table, or an example of it? E.g.:

sample case gene1 gene2 sample1 0 YY YY ...