GEE makes sense when you have lots of samples in the population, measured
many times, and wish to know about population-level effects of a small
number of factors; it's an alternative to other methods of
hierarchical/nested mixed models.  In the limma moderated-ANOVA world,
you'd use duplicateCorrelation to account for the nested correlation
structure, while still getting the (often huge) benefits of between-gene
moderation of variance you get with limma.

  http://www.jstatsoft.org/v15/i02/paper

If this were a reviewer making the criticism/suggestion, I'd respond with a
sensitivity analysis: to what degree are your inferred results sensitive to
choices in modeling (limma with/without duplicateCorrelation; limma vs.
unmoderated straight-up glm; glm vs. geepack error structures).  Based on
that, I'd argue whether it even matters, and if it does matter, show data
for an example gene or two for which the modeling choice has a strong
effect on estimates of magnitude and significance.  There's never any one
single "right" answer -- "all models are wrong; but some are useful"
(George Box).

-Aaron

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