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Statistical resolution of ambiguous HLA typing data.

Listgarten J, Brumme Z, Kadie C, Xiaojiang G, Walker B, Carrington M, Goulder P, Heckerman D - PLoS Comput. Biol. (2008)

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: Microsoft Research, Redmond, Washington, United States of America. jennl@microsoft.com

ABSTRACT
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.

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Low Resolution Prediction.Each set of grouped bars represents, from darkest to lightest, respectively, 100% mask with softmax model, 100% mask with allele marginals model, 30% mask with softmax model, 30% mask with allele marginals model. The number of masked alleles for the 100% mask was 4254, 1429, and 1062, and for the 30% mask, 1287, 477, and 306, in the African, Asian, and Hispanic test sets, respectively.
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pcbi-1000016-g006: Low Resolution Prediction.Each set of grouped bars represents, from darkest to lightest, respectively, 100% mask with softmax model, 100% mask with allele marginals model, 30% mask with softmax model, 30% mask with allele marginals model. The number of masked alleles for the 100% mask was 4254, 1429, and 1062, and for the 30% mask, 1287, 477, and 306, in the African, Asian, and Hispanic test sets, respectively.

Mentions: Finally, in some instances, only low-resolution data (i.e., two-digit resolution) is available. Consequently, we investigated the prediction accuracy of our algorithm in this situation—that is, when 100% of the alleles were masked to two-digit. The results for the private African, Asian, and Hispanic data sets are shown in Figure 6. Because of the large number of allele combinations in the European data set, it was not possible to perform this experiment in a reasonable amount of time using the current sequential implementation of the algorithm. This problem should not be a big concern, however, as the algorithm can be easily parallelized.


Statistical resolution of ambiguous HLA typing data.

Listgarten J, Brumme Z, Kadie C, Xiaojiang G, Walker B, Carrington M, Goulder P, Heckerman D - PLoS Comput. Biol. (2008)

Low Resolution Prediction.Each set of grouped bars represents, from darkest to lightest, respectively, 100% mask with softmax model, 100% mask with allele marginals model, 30% mask with softmax model, 30% mask with allele marginals model. The number of masked alleles for the 100% mask was 4254, 1429, and 1062, and for the 30% mask, 1287, 477, and 306, in the African, Asian, and Hispanic test sets, respectively.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC2289775&req=5

pcbi-1000016-g006: Low Resolution Prediction.Each set of grouped bars represents, from darkest to lightest, respectively, 100% mask with softmax model, 100% mask with allele marginals model, 30% mask with softmax model, 30% mask with allele marginals model. The number of masked alleles for the 100% mask was 4254, 1429, and 1062, and for the 30% mask, 1287, 477, and 306, in the African, Asian, and Hispanic test sets, respectively.
Mentions: Finally, in some instances, only low-resolution data (i.e., two-digit resolution) is available. Consequently, we investigated the prediction accuracy of our algorithm in this situation—that is, when 100% of the alleles were masked to two-digit. The results for the private African, Asian, and Hispanic data sets are shown in Figure 6. Because of the large number of allele combinations in the European data set, it was not possible to perform this experiment in a reasonable amount of time using the current sequential implementation of the algorithm. This problem should not be a big concern, however, as the algorithm can be easily parallelized.

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: Microsoft Research, Redmond, Washington, United States of America. jennl@microsoft.com

ABSTRACT
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.

Show MeSH