In Silico Resolution of Ambiguous HLA Typing Data - Listgarten, Jennifer; Brumme, Zabrina; Kadie, Carl; Xiaojiang, Gao; Walker, Bruce; Carrington, Mary; Goulder, Phillip; Heckerman, David (2008)
High-resolution HLA typing plays a central role in many areas of immunology, such as transplant matching, identifying immunogenetic risk factors for disease, studying how the genomes of pathogens evolve in response to immune selection pressures, and vaccine design. However, high-resolution HLA typing is frequently unavailable due to its high cost or the inability to re-type historical data. We recently introduced and evaluated a method for statistical, in silico refinement of ambiguous and/or low-resolution HLA data (Listgarten, et al. 2008). Here we present a summary of this work for the histocompatibility community. A tool based on our approach is available for research purposes at http://microsoft.com/science. The user selects an appropriate population of interest (i.e., African, Amerindian, Asian, European, Hispanic) and uploads the low-resolution/ambiguous data; our server then returns the statistically refined version of the HLA data.