Limits...
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.

Show MeSH
Results for population-augmented model.Abbreviations are: SSC = softmax+simple+conjunctive, SS = softmax+simple, S = simple, RM = regularized multinomial, M = non-regularized multinomial, AM = allele marginals, S = separate. The number of masked alleles in the test set was 514. For all methods, except ‘separate’, a single model was trained on data from all ethnicities.
© Copyright Policy
Related In: Results  -  Collection


getmorefigures.php?uid=PMC2289775&req=5

pcbi-1000016-g003: Results for population-augmented model.Abbreviations are: SSC = softmax+simple+conjunctive, SS = softmax+simple, S = simple, RM = regularized multinomial, M = non-regularized multinomial, AM = allele marginals, S = separate. The number of masked alleles in the test set was 514. For all methods, except ‘separate’, a single model was trained on data from all ethnicities.

Mentions: To determine whether leveraging information across populations is useful, we compared our leveraged population models to those built separately on each population. We did so on data from dbMHC, which contains a diverse set of populations. (We excluded the Irish population because this population is extremely homogeneous relative to the others.) Recall that we introduced two types of leveraging features: simple and conjunctive. We used our softmax model both with the simple features alone, and with both the simple and the conjunctive features, as shown in Figure 3.


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)

Results for population-augmented model.Abbreviations are: SSC = softmax+simple+conjunctive, SS = softmax+simple, S = simple, RM = regularized multinomial, M = non-regularized multinomial, AM = allele marginals, S = separate. The number of masked alleles in the test set was 514. For all methods, except ‘separate’, a single model was trained on data from all ethnicities.
© Copyright Policy
Related In: Results  -  Collection

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

pcbi-1000016-g003: Results for population-augmented model.Abbreviations are: SSC = softmax+simple+conjunctive, SS = softmax+simple, S = simple, RM = regularized multinomial, M = non-regularized multinomial, AM = allele marginals, S = separate. The number of masked alleles in the test set was 514. For all methods, except ‘separate’, a single model was trained on data from all ethnicities.
Mentions: To determine whether leveraging information across populations is useful, we compared our leveraged population models to those built separately on each population. We did so on data from dbMHC, which contains a diverse set of populations. (We excluded the Irish population because this population is extremely homogeneous relative to the others.) Recall that we introduced two types of leveraging features: simple and conjunctive. We used our softmax model both with the simple features alone, and with both the simple and the conjunctive features, as shown in Figure 3.

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