Multi-Population Classical HLA Type Imputation

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. Author Summary: The human leukocyte antigen (HLA) proteins influence how pathogens and components of body cells are presented to immune cells. It has long been known that they are highly variable and that this variation is associated with differential risk for autoimmune and infectious diseases. Variant frequencies differ substantially between and even within continents. Determining HLA genotypes is thus an important part of many studies to understand the genetic basis of disease risk. However, conventional methods for HLA typing (e.g. targeted sequencing, hybridisation, amplification) are typically laborious and expensive. We have developed a method for inferring an individual's HLA genotype based on evaluating genetic information from nearby variable sites that are more easily assayed, which aims to integrate heterogeneous data. We introduce two key innovations: we allow for single HLA types to appear on heterogeneous backgrounds of genetic information and we take into account the possibility of genotyping error, which is common within the HLA region. We show that the method is well-suited to deal with multi-population datasets: it enables integrated HLA type inference for individuals of differing ancestry and ethnicity. It will therefore prove useful particularly in international collaborations to better understand disease risks, where samples are drawn from multiple countries.