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Publication Details
AFRICAN RESEARCH NEXUS
SHINING A SPOTLIGHT ON AFRICAN RESEARCH
agricultural and biological sciences
Statistical resolution of ambiguous HLA typing data
PLoS Computational Biology, Volume 4, No. 2, Year 2008
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Description
High-resolution HLA typing plays a central role in many areas of immunology, such as in identifying immunogenetic risk factors for disease, in studying how the genomes of pathogens evolve in response to immune selection pressures, and also in vaccine design, where identification of HLA-restricted epitopes may be used to guide the selection of vaccine immunogens. Perhaps one of the most immediate applications is in direct medical decisions concerning the matching of stem cell transplant donors to unrelated recipients. However, high-resolution HLA typing is frequently unavailable due to its high cost or the inability to re-type historical data. In this paper, we introduce and evaluate a method for statistical, in silico refinement of ambiguous and/or low-resolution HLA data. Our method, which requires an independent, high-resolution training data set drawn from the same population as the data to be refined, uses linkage disequilibrium in HLA haplotypes as well as four-digit allele frequency data to probabilistically refine HLA typings. Central to our approach is the use of haplotype inference. We introduce new methodology to this area, improving upon the Expectation-Maximization (EM)-based approaches currently used within the HLA community. Our improvements are achieved by using a parsimonious parameterization for haplotype distributions and by smoothing the maximum likelihood (ML) solution. These improvements make it possible to scale the refinement to a larger number of alleles and loci in a more computationally efficient and stable manner. We also show how to augment our method in order to incorporate ethnicity information (as HLA allele distributions vary widely according to race/ethnicity as well as geographic area), and demonstrate the potential utility of this experimentally. A tool based on our approach is freely available for research purposes at http://microsoft.com/science. © 2008 Listgarten et al.
Authors & Co-Authors
Listgarten, Jennifer
United States, Redmond
Microsoft Research
Brumme, Zabrina L.
United States, Boston
Massachusetts General Hospital
Kadie, Carl M.
United States, Redmond
Microsoft Research
Walker, Bruce D.
United States, Boston
Massachusetts General Hospital
United States, Chevy Chase
Howard Hughes Medical Institute
Carrington, Mary N.
United States, Frederick
National Cancer Institute at Frederick
Goulder, Philip Jeremy Renshaw
United States, Boston
Massachusetts General Hospital
United Kingdom, Oxford
University of Oxford
Heckerman, David E.
United States, Redmond
Microsoft Research
Statistics
Citations: 63
Authors: 7
Affiliations: 5
Identifiers
Doi:
10.1371/journal.pcbi.1000016
ISSN:
1553734X
Research Areas
Genetics And Genomics
Study Design
Cross Sectional Study
Case-Control Study