I have consulted to National Marrow Donor Program/Be The Match [0] off and on for several years. There are typing labs using long reads but most reporting/matching/analysis is still performed at the nomenclature level [1].
I hope in the near future we'll be able to simply assemble the entire MHC for each sample, as messy as it might be, see e.g., "A diploid assembly-based benchmark for variants in the major histocompatibility complex" [2].
[0] https://bethematch.org [1] https://www.ebi.ac.uk/ipd/imgt/hla [2] https://www.nature.com/articles/s41467-020-18564-9
But we know less associations about them. Same applies to TCR genes. A chicken-and-egg problem, we need good massive GWAS to find out.
My background is in CS, AI and statistics. But I've done lots of graduate research in genetics and epigenetics. I'm very interested in understanding the interactions between HLA and commensal / pathogen epitopes in health & disease. Also in vaccine design.
How about you? I can see from your posts you are with the Big Data Genomics team at UC Berkeley AMPLab.
HLA associations with autoimmune disorders are extraordinarily strong. Same applies to infectious diseases, vaccine efficiency and checkpoint inhibitor efficiency.
While you can type HLA with classical techniques, the only really reliable way is really to use long reads.
Same applies to CYP enzyme superfamily, where variation is linked to some rare drug toxicity events for example.
We should all know our HLA and our CYP genotypes. Why 23andme does not even attempt to impute HLA is beyond my understanding.