Is there an published method for removing the effect of isolation by distance from genetic distances?

You can kind of do this with Mantel tests and related methods. As always there are issues.

If we look at this publication, we for example find a similar motivation:

Another possibility for using the Mantel test is to compare the relationship between two matrices, but taking into account the effect of a third one (usually the geographical distances), as originally proposed by Smouse et al. (1986). When analyzing spatially distributed data, the main issue is to find out if the two matrices are “causally” related (i.e., in the sense that they indicate an ecological or evolutionary process), or if the observed relationship appears only because both variables are spatially structured by intrinsic effects (i.e., distance-structured dispersal causing more similarity between neighboring populations).

In this case, you would be correcting in some way for the effects of isolation-by-distance, probably using some measure of geographic distance as the third matrix in question. However, Mantel tests can't really be interpreted in terms of variance partitioning, which is what you seem to want.

There are more spatial regression methods available that may be able to do this, though they seem to be less popular (probably because Mantel is simple and relatively assumption-free). They treat the isolation by distance effect as a spatial autocorrelation, which makes a certain degree of sense. If you are interested in a variance-partitioning type analysis, these are probably a better bet than Mantel.

But there are issues because it is not clear how to interpret isolation by distance in the absence of the genetic distances; simple distance is probably not good enough, and there are multiple plausible formulations of the distancing mechanism that are not restricted to naive measures of distance. All this is to say, you can do this, but beware of the assumptions that you're making going in.