Accurate multi-population imputation of MICA, MICB, HLA-E, HLA-F and HLA-G alleles from genome SNP data

TL;DR

This paper introduces accurate methods to infer specific MHC gene alleles from SNP data, enabling better understanding of immune-related diseases.

Contribution

The study introduces high-accuracy imputation models for non-classical HLA and MICA/MICB genes using multi-population references and common SNP arrays.

Findings

Imputation models achieved a mean accuracy of 99.3% for MICA, MICB, HLA-E, HLA-F, and HLA-G alleles.

Validation against clinical-grade data showed 99.8% accuracy for 1000 Genomes exome allele calling.

Models tailored for two SNP arrays achieved mean accuracies of 99.1% and 98.9%.

Abstract

In addition to the classical HLA genes, the major histocompatibility complex (MHC) harbors a high number of other polymorphic genes with less established roles in disease associations and transplantation matching. To facilitate studies of the non-classical and non-HLA genes in large patient and biobank cohorts, we trained imputation models for MICA, MICB, HLA-E, HLA-F and HLA-G alleles on genome SNP array data. We show, using both population-specific and multi-population 1000 Genomes references, that the alleles of these genes can be accurately imputed for screening and research purposes. The best imputation model for MICA, MICB, HLA-E, -F and -G achieved a mean accuracy of 99.3% (min, max: 98.6, 99.9). Furthermore, validation of the 1000 Genomes exome short-read sequencing-based allele calling against a clinical-grade reference data showed an average accuracy of 99.8%, testifying for the quality of the 1000 Genomes data as an imputation reference. We also fitted the models for Infinium Global Screening Array (GSA, Illumina, Inc.) and Axiom Precision Medicine Research Array (PMRA, Thermo Fisher Scientific Inc.) SNP content, with mean accuracies of 99.1% (97.2, 100) and 98.9% (97.4, 100), respectively.

The major histocompatibility complex (MHC) region on chromosome 6 significantly influences disease risk, particularly in autoimmune conditions. To improve fine-mapping of potentially causal genetic variants within this region, we developed accurate imputation methods for inferring functional allelic variation from MHC SNP data. While existing tools primarily focus on classical HLA genes, our study extends to non-classical HLA genes (HLA-E, HLA-F, and HLA-G) and MHC Class I Chain-Related MIC genes (MICA and MICB) which have specific functions in innate and adaptive immunity. Leveraging population-specific Finnish and multi-population 1000 Genomes references, our imputation models demonstrate high accuracy. Moreover, we tailored models for two widely used genome SNP arrays: the Infinium Global Screening Array (Illumina, Inc.) and the Axiom Precision Medicine Research Array (Thermo Fisher Scientific Inc.). These freely available, multi-population models empower researchers to explore genetic MHC associations in more detail and contribute to our understanding of immune-related disease mechanisms.

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