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

Author

Listed:
  • Tatsuhiko Naito

    (Osaka University Graduate School of Medicine
    The University of Tokyo)

  • Ken Suzuki

    (Osaka University Graduate School of Medicine)

  • Jun Hirata

    (Osaka University Graduate School of Medicine
    Pharmaceutical Discovery Research Laboratories, Teijin Pharma Limited)

  • 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
    Osaka University
    Osaka University)

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.

Suggested Citation

  • Tatsuhiko Naito & Ken Suzuki & Jun Hirata & Yoichiro Kamatani & Koichi Matsuda & Tatsushi Toda & Yukinori Okada, 2021. "A deep learning method for HLA imputation and trans-ethnic MHC fine-mapping of type 1 diabetes," Nature Communications, Nature, vol. 12(1), pages 1-14, December.
    • Handle: RePEc:nat:natcom:v:12:y:2021:i:1:d:10.1038_s41467-021-21975-x
    DOI: 10.1038/s41467-021-21975-x
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    Cited by:

    1. Kyuto Sonehara & Yoshitaka Yano & Tatsuhiko Naito & Shinobu Goto & Hiroyuki Yoshihara & Takahiro Otani & Fumiko Ozawa & Tamao Kitaori & Koichi Matsuda & Takashi Nishiyama & Yukinori Okada & Mayumi Sug, 2024. "Common and rare genetic variants predisposing females to unexplained recurrent pregnancy loss," Nature Communications, Nature, vol. 15(1), pages 1-9, December.

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