| Title: | A deep learning method for HLA imputation and trans-ethnic MHC fine-mapping of type 1 diabetes |
| Journal: | Nature Communications |
| Published: | 12 Mar 2021 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/33712626/ |
| DOI: | https://doi.org/10.1038/s41467-021-21975-x |
| URL: | https://www.nature.com/articles/s41467-021-21975-x.pdf |
| Citations: | 57 (26 in last 2 years) as of 8 Aug 2024 |
Publication 3867
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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.</p>
7 Authors
- Tatsuhiko Naito
- Ken Suzuki
- Jun Hirata
- Yoichiro Kamatani
- Koichi Matsuda
- Tatsushi Toda
- Yukinori Okada
Enabling scientific discoveries that improve human health