Recommendations for missing value imputation - DNA methylation data I'm looking for some options for imputation for a high-dimensional dataset of DNA methylation (bisulfite sequencing) data.  Dimensions on the order of 50-100 samples x ~500,000 CpG loci/features.

I've used K-nearest neighbors, but It seems that this method is not very accurate. As far as I can tell, it limits the minimum number of "genes/features" to impute at one time to something like 1500. which is kind of a lot.  

I've also used missForest to impute smaller datasets with greater accuracy, but it seems computationally infeasible to do it on the full 50x500,000 dataset.

I need to impute values because I'm using the data for some statistical modeling which require complete cases.  

Anyone know of better alternatives than K-nearest neighbor for large scale imputation?
I think this is still an active field of research.  I have heard of Phenix, which might be appropriate.