First, I don't really understand the experiment. BUT if you have 3 different outcome measure variables like S1, S2 and S3 that you think should be treated then perhaps you want to reshape the data so that you have a variable to indicate the stage and a single "measure" value.
So if your current data looks like
Mouse line S1 S2 S3
A 1 15 14 14.5 (obviously very made up numbers)
Then reshape to
Mouse Line Stage Measure
A 1 S1 15
A 1 S2 14
A 1 S3 14.5
Which would be a task for Proc transpose something like:
Proc transpose data=have out=trans
prefix=measure;
by mouse line;
var s1 s2 s3;
run;
The behavior of simple transpose code would add a variable _name_ with the value S1 S2 S3, the name of the original variable. Options would allow renaming it to Stage to make more sense.
Obviously your data and code would also include your Treat variable (and possibly others).
Then
1) change the data set name to the transposed set
2) add Stage (or _name_ or whatever) to the Class statement.
3) change the model statement to use Measure (or which name you get in the transposed data) as the dependent and add Stage (or whatever) to the independent side.
LSMEANS, Estimate and Contrast statements let you build "questions" about combinations of the variables in the model.
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