A deep learning method for HLA imputation and trans-ethnic MHC fine-mapping of type 1 diabetes

Conventional human leukocyte antigen (HLA) imputation methods drop their performance for infrequent alleles, which is one of the factors that reduce the reliability of trans-ethnic major histocompatibility complex (MHC) fine-mapping due to inter-ethnic heterogeneity in allele frequency spectra. We develop DEEP*HLA, a deep learning method for imputing HLA genotypes. Through validation using the Japanese and European HLA reference panels (n=1,118 and 5,122), DEEP*HLA achieves the highest accuracies with significant superiority for low-frequency and rare alleles. DEEP*HLA is less dependent on distance-dependent linkage disequilibrium decay of the target alleles and might capture the complicated region-wide information. We apply DEEP*HLA to type 1 diabetes GWAS data from BioBank Japan (n=62,387) and UK Biobank (n=354,459), and successfully disentangle independently associated class I and II HLA variants with shared risk among diverse populations (the top signal at amino acid position 71 of HLA-DR beta 1; P=7.5 x 10(-120)). Our study illustrates the value of deep learning in genotype imputation and trans-ethnic MHC fine-mapping. Human leukocyte antigen (HLA) genes contribute to risk of many complex traits, yet understanding inter-ethnic heterogeneity is computationally challenging. Here, the authors develop DEEP*HLA for imputation of HLA genotypes and show its ability to disentangle HLA variant risk effects in diverse populations.

Automated summary

The study presents a deep learning method called DEEP*HLA for imputing HLA genotypes, which achieves high accuracy, especially for low-frequency and rare alleles. The results demonstrate that DEEP*HLA can successfully disentangle HLA variant risk effects in diverse populations.