We developed a new highthroughput approach combining next-generation squencing, mass spectrometry and bioinformatics that speeds up the identification of MiHAs. We show that personalized database allow identifications of MiHAs by comparing information coming from two individuals. By comparing 2 HLA-identical siblings we identified: (1) 23 MiHAs caused by SNPs in the peptide-coding region (6 are validated); (2) 41 MiHAs whose presence or abscence does not correlate with SNPs in the genomic sequence. They can originate from cis- or trans-acting genetic polymorphisms or epigenetic mechanisms; (3) Potential MiHAs in the general population resulting from reported SNPs in the peptide-coding region. Less than 0.1% of the non-synonymous SNPs between the 2 siblings sequenced at the exome/transciptome level can be viewed from a T cell perspective.
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