In R, you can use quasipoisson or quasibinomial for overdispersed Poisson or Binomial GLMs. But what if you have overdispersed Gamma GLM or other continuous variables?
The gamma already has a dispersion parameter, so there's no "overdispersed" case. It's like the normal in that sense (where $\sigma^2$ is a parameter, so there's no "overdispersed" Gaussian).
However, the readily available estimate of the dispersion parameter from the GLM output isn't ML (more generally the ML estimates for dispersion parameters in a GLM don't work well; the Poisson is a classic example). You can find an ML estimate, though, if you need it.
(If you're an R user, the package MASS that comes with R has a function for estimating the shape parameter after a GLM fit.)