TY - JOUR T1 - Statistical Resolution of Ambiguous HLA Typing Data A1 - Listgarten, Jennifer A1 - Brumme, Zabrina A1 - Kadie, Carl A1 - Xiaojiang, Gao A1 - Walker, Bruce A1 - Carrington, Mary A1 - Goulder, Philip A1 - Heckerman, David Y1 - 2008/02/29 N2 - Author SummaryAt the core of the human adaptive immune response is the train-to-kill mechanism in which specialized immune cells are sensitized to recognize small peptides from foreign sources (e.g., from HIV or bacteria). Following this sensitization, these immune cells are then activated to kill other cells which display this same peptide (and which contain this same foreign peptide). However, in order for sensitization and killing to occur, the foreign peptide must be “paired up” with one of the infected person's other specialized immune molecules—an HLA molecule. The way in which peptides interact with these HLA molecules defines if and how an immune response will be generated. There is a huge repertoire of such HLA molecules, with almost no two people having the same set. Furthermore, a person's HLA type can determine their susceptibility to disease, or the success of a transplant, for example. However, obtaining high quality HLA data for patients is often difficult because of the great cost and specialized laboratories required, or because the data are historical and cannot be retyped with modern methods. Therefore, we introduce a statistical model which can make use of existing high-quality HLA data, to infer higher-quality HLA data from lower-quality data. JF - PLOS Computational Biology JA - PLOS Computational Biology VL - 4 IS - 2 UR - https://doi.org/10.1371/journal.pcbi.1000016 SP - e1000016 EP - PB - Public Library of Science M3 - doi:10.1371/journal.pcbi.1000016 ER -