building a formula for glm() with 30,000 independent variables

I would like to use R to perform a logistic regression with about
30,000 independent variables.  That's right, thirty thousand.  Most
will be irrelevant: the intent is to use the regression to identify
the few that actually matter.

Among other things, this calls for giving glm() a colossal "y ~ ..."
formula with thirty thousand summed terms on its right hand side.  I
build up the formula as a string and then call as.formula() to convert
it.  Unfortunately, the conversion fails.  The parser reports that it
has overflowed its stack.  :-(

Is there any way to pull this off in R?  Can anyone suggest
alternatives to glm() or to R itself that might be capable of handling
a problem of this size?  Or am I insane to even be considering an
analysis like this?

Thanks!

It would be worth doing a simulation first to see if ANY statistical properties of the resulting estimates or P-values work as advertised with your setup.  I would expect severe biases, lack of preservation of type I error, and low probability of selecting the "correct" variables.  The "few that actually matter" will likely be those whose estimates are made with the most error.