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

Tatsuhiko Naito, Ken Suzuki, Jun Hirata, Yoichiro Kamatani, Koichi Matsuda, Tatsushi Toda and Yukinori Okada ()
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Tatsuhiko Naito: Osaka University Graduate School of Medicine
Ken Suzuki: Osaka University Graduate School of Medicine
Jun Hirata: Osaka University Graduate School of Medicine
Yoichiro Kamatani: The University of Tokyo
Koichi Matsuda: The University of Tokyo
Tatsushi Toda: The University of Tokyo
Yukinori Okada: Osaka University Graduate School of Medicine

Nature Communications, 2021, vol. 12, issue 1, 1-14

Abstract: Abstract 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-DRβ1; P = 7.5 × 10−120). Our study illustrates the value of deep learning in genotype imputation and trans-ethnic MHC fine-mapping.

Date: 2021
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DOI: 10.1038/s41467-021-21975-x

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