I have a dataset where I want to see effect of a drug on my patients who responded and not responded towards treatment. I collected their blood at three different time point or visit. For each patient I have their age and sex information with me. Now to perform differential expression analysis I used DESeq2 to perform time series analysis as I have collected blood at three different visit. I want to control age and gender effect on my data so I can see interaction between responder group and different time point. Here is the sample table and my DESeq2 design formula:

sample     Phenotype     visit     Age     Gender

1             NonResponder 1      42        female
2             NonResponder  2      42        female
3            NonResponder   3      42        female
4            NonResponder   1      49       female
5           NonResponder    2     49        female
6           NonResponder   3     49       female
7          NonResponder    1     27       male
8          NonResponder     2     27       male
9         NonResponder      3     27      male
10        Responder         1       77      female
11       Responder          2      77      female
12       Responder         3       77       female
13       Responder         1       51      male
14      Responder         2       51       male
15      Responder        3       51        male
16      Responder        1       47        male
17      Responder        2       47        male
18      Responder       3        47        male

So which design should I use to control age and gender effect on my data

design 1:

dds=(design= ~age+gender+visit+phenotype+visit:phenotype+age:phenotype+gender:phenotype)
dds=DESeq(dds)

design 2:

dds=(design=~age+gender+visit+phenotype+visit:phenotype)
dds=DESeq(dds,test="LRT", reduced=~age+gender)

I will highly appreciate help with this

Best,

Lalit

The second design is what it typically done to "control" for certain variables while testing the effect of others. 

That will find any genes where visit or phenotype have any effect, including only an interaction.

I don't like to add "age" alone to the design as it implies that log gene expression increases linearly with years of life. For simple modeling, where users aren't familiar with more advanced approaches, I'd recommend to break age into 3-5 groups using cut() and use this factor variable instead. Here you probably only have enough samples cut 3 groups of age.