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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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Four-loci, dbMHC Irish data.Abbreviations are: S = softmax, RM = regularized multinomial, M = non-regularized multinomial, AM = allele marginals. A total of 468 alleles were masked in the test set.
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pcbi-1000016-g007: Four-loci, dbMHC Irish data.Abbreviations are: S = softmax, RM = regularized multinomial, M = non-regularized multinomial, AM = allele marginals. A total of 468 alleles were masked in the test set.

Mentions: We compared our methods on data with four loci, spanning the HLA-A, -B, -C and -DRB1 loci. The four-loci data available to us, with the largest sample size, was the Irish set in dbMHC. As shown in Figure 7, we see that the relative performance of the methods is roughly the same as in earlier experiments. Given that LD may not be as strong between class I and class II alleles, it is of interest to determine how well each locus can be predicted. Thus we used a locus-specific masking, as described earlier. The accuracy at each of the HLA-A, -B, -C and -DRB1 alleles was respectively 97%, 98%,99%, and 80%. This indicates that there is not sufficient linkage between the HLA-A, -B, -C loci and the HLA -DRB1 locus to accurately resolve ambiguity at the DRB1 locus. However, it may be the case that with additional class II loci, refinement of class II data would be feasible.


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)

Four-loci, dbMHC Irish data.Abbreviations are: S = softmax, RM = regularized multinomial, M = non-regularized multinomial, AM = allele marginals. A total of 468 alleles were masked in the test set.
© Copyright Policy
Related In: Results  -  Collection

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

pcbi-1000016-g007: Four-loci, dbMHC Irish data.Abbreviations are: S = softmax, RM = regularized multinomial, M = non-regularized multinomial, AM = allele marginals. A total of 468 alleles were masked in the test set.
Mentions: We compared our methods on data with four loci, spanning the HLA-A, -B, -C and -DRB1 loci. The four-loci data available to us, with the largest sample size, was the Irish set in dbMHC. As shown in Figure 7, we see that the relative performance of the methods is roughly the same as in earlier experiments. Given that LD may not be as strong between class I and class II alleles, it is of interest to determine how well each locus can be predicted. Thus we used a locus-specific masking, as described earlier. The accuracy at each of the HLA-A, -B, -C and -DRB1 alleles was respectively 97%, 98%,99%, and 80%. This indicates that there is not sufficient linkage between the HLA-A, -B, -C loci and the HLA -DRB1 locus to accurately resolve ambiguity at the DRB1 locus. However, it may be the case that with additional class II loci, refinement of class II data would be feasible.

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