Working with MHC, and it is more sensible to create a haplotype network based on the variability in amino acid sequences instead of the DNA sequences, since multiple DNA haplotypes code for the same amino acid haplotype.
You may consider working with DNA sequences after all since recombination rates will act on the SNP variation rather than at the amino acid level - you may get more meaningful data and be able to (more easily) genetically segregate the relationship between those animals with the same MHC DNA haplotype.
What are haplotype networks? I assume you are talking about functional overlaps leading to super-types for super antigen function. I have written few papers to compress function using structure data, you may take a look.
MHC antigens are homologous across species. Is your interest is in class I or class II? The first step would be to create a dataset of known avian MHC antigen proteins. MSA is next step. Phylogeny is 3rd step. You can get a tree for relatedness within across geography. You may focus on the peptide binding groove and you may see more convergence.
If you are looking for free software in this regard, the SWISSPROT VIEWER is one such reliable software, Otherwise there many softwares in the market which you may buy at some prices tagged on them.
In principle ,I would use the DNA sequences. Aminoacid sequences contain less information due to genetic code redundancy. For the same reason, AA sequences are more homoplasious than DNA ones.
I agree with previous comments that you might want to consider classifying your MHC DNA sequence variants as 'supertypes' according to which particular types of peptides they encode. There are several methods to classify MHC supertypes (Search: MHC supertypes, Doytchinova & Flower 2005; Reche & Reinherz 2007; Sidney et al. 2008). If you base your analysis on MHC supertupes (i.e. DNA sequence veriants) you could then use the software 'Arlequin' and 'HapStar' (http://fo.am/hapstar/) to generate haplotype (supertype) networks.