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Open Access Research article

Empirical evaluations of analytical issues arising from predicting HLA alleles using multiple SNPs

Xinyi Cindy Zhang1, Shuying Sue Li1, Hongwei Wang1, John A Hansen23 and Lue Ping Zhao1*

Author affiliations

1 Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, 1100 Fairview Ave N, Seattle, WA 98109, USA

2 Division of Clinical Research, Fred Hutchinson Cancer Research Center, 1100 Fairview Ave N, Seattle, WA 98109, USA

3 School of Medicine, University of Washington, 1959 NE Pacific Street, Seattle, WA 98195, USA

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Citation and License

BMC Genetics 2011, 12:39  doi:10.1186/1471-2156-12-39

Published: 25 April 2011

Abstract

Background

Numerous immune-mediated diseases have been associated with the class I and II HLA genes located within the major histocompatibility complex (MHC) consisting of highly polymorphic alleles encoded by the HLA-A, -B, -C, -DRB1, -DQB1 and -DPB1 loci. Genotyping for HLA alleles is complex and relatively expensive. Recent studies have demonstrated the feasibility of predicting HLA alleles, using MHC SNPs inside and outside of HLA that are typically included in SNP arrays and are commonly available in genome-wide association studies (GWAS). We have recently described a novel method that is complementary to the previous methods, for accurately predicting HLA alleles using unphased flanking SNPs genotypes. In this manuscript, we address several practical issues relevant to the application of this methodology.

Results

Applying this new methodology to three large independent study cohorts, we have evaluated the performance of the predictive models in ethnically diverse populations. Specifically, we have found that utilizing imputed in addition to genotyped SNPs generally yields comparable if not better performance in prediction accuracies. Our evaluation also supports the idea that predictive models trained on one population are transferable to other populations of the same ethnicity. Further, when the training set includes multi-ethnic populations, the resulting models are reliable and perform well for the same subpopulations across all HLA genes. In contrast, the predictive models built from single ethnic populations have superior performance within the same ethnic population, but are not likely to perform well in other ethnic populations.

Conclusions

The empirical explorations reported here provide further evidence in support of the application of this approach for predicting HLA alleles with GWAS-derived SNP data. Utilizing all available samples, we have built "state of the art" predictive models for HLA-A, -B, -C, -DRB1, -DQB1 and -DPB1. The HLA allele predictive models, along with the program used to carry out the prediction, are available on our website.