An accessible explanation of what’s doable and not doable and likely erroneously doable with subsamples is https://www.stata-journal.com/article.html?article=st0153.
If you rely on replicate weights, I would argue you don’t have to use the full sample.
Full sample does include the group quarter observations; you can use RELATE variable or GQTYPE to identify them.
As a preliminary step, I would analyze my models with the main weight only [perwt] as far as I can recall off the top of my head what the variable name is, and expect that my standard errors could be off by a factor of at most 2. Then once I have models that I am satisfied with, I would move on to the full analysis. To make it easier in Stata, you can have
* fake settings: uncomment for prelim analysis
* svyset [pw=perwt]
* real settings: uncomment for the ultimate analysis
* svyset [pw=perwt], vce(sdr) sdrw(perwtp1-perwtp80) mse
svy : model whatever
Then when the time comes, you can run it with the real thing.