Statistical imputation of classical HLA alleles in case-control studies has become established as a valuable tool for identifying and fine-mapping signals of disease association in the MHC. Imputation into diverse populations has, however, remained challenging, mainly because of the additional haplotypic heterogeneity introduced by combining reference panels of different sources. We present an HLA type imputation model, HLA*IMP:02, designed to operate on a multi-population reference panel. HLA*IMP:02 is based on a graphical representation of haplotype structure. We present a probabilistic algorithm to build such models for the HLA region, accommodating genotyping error, haplotypic heterogeneity and the need for maximum accuracy at the HLA loci, generalizing the work of Browning and Browning (2007) and Ron et al. (1998). HLA*IMP:02 achieves an average 4-digit imputation accuracy on diverse European panels of 97% (call rate 97%). On non-European samples, 2-digit performance is over 90% for most loci and ethnicities where data available. HLA*IMP:02 supports imputation of HLA-DPB1 and HLA-DRB3-5, is highly tolerant of missing data in the imputation panel and works on standard genotype data from popular genotyping chips. It is publicly available in source code and as a user-friendly web service framework.

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Journal article


PLoS Comput Biol

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Computational Biology, Continental Population Groups, Genetics, Population, HLA Antigens, Haplotypes, Humans, Models, Genetic, Models, Immunological, Polymorphism, Single Nucleotide, Principal Component Analysis, Reproducibility of Results, Software