ArticleNature communications2021
A deep learning method for HLA imputation and trans-ethnic MHC fine-mapping of type 1 diabetes.
Article in Nature communications, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 49 papers, 1 of them a synthesis that pooled it.
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Who cites it
49 citing papers in PubMed, 1 synthesis or guideline pooled it, 79 citations in OpenAlex.
- Contribution of germline and somatic mutations to risk of neuromyelitis optica spectrum disorder.Cell genomics · 2025Pooled it
- Multiancestry genome-wide association and multiomics analyses elucidate spatiocellular features of multiple sclerosis genetics.Nature genetics · 2026Article
- Development and application of type 1 diabetes polygenic scores across diverse populations.Diabetologia · 2026Review
- Exploring the use of machine and deep learning in genome-wide association studies: a comprehensive review.BioData mining · 2026Review
- Predominant HLA class I associations with lupus nephritis susceptibility, histology class and reduced renal function in patients with systemic lupus erythematosus: a retrospective case-control study.Rheumatology international · 2026Article
- Unravelling the genetic architecture of cerebral small vessel disease in the context of stroke.Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism · 2026Review
- ALGI: Sparse Convolutional Denoising Autoencoder Utilizing Local Genomic Information for Genotype Imputation.Animals : an open access journal from MDPI · 2026Article
- The digital keystone: how artificial intelligence is reshaping HLA research and clinical practice.Immunogenetics · 2026Review
- AI solutions for evolutionary genomics of nonmodel species.Evolution letters · 2026Review
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- Elucidating genetic backgrounds of myasthenia gravis in Japanese by genome-wide association studies and multi-omics analyses of thymoma.Nature communications · 2026Article
- Computational identification of B- and T-cell epitopes: a unified task taxonomy and review of databases, datasets, predictive pipelines, and gaps.Frontiers in immunology · 2026Review
- HLA-DQB1*06:02 and a critical amino acid variant in protection against occult hepatitis B virus infection.Frontiers in immunology · 2026Article
- Preclinical Diagnosis of Type 1 Diabetes: Reality or Utopia.Biomedicines · 2025Review
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- Deep learning in next-generation vaccine development for infectious diseases.Molecular therapy. Nucleic acids · 2025Review
- Deciphering state-dependent immune features from multi-layer omics data at single-cell resolution.Nature genetics · 2025Article
- Germline variants and mosaic chromosomal alterations affect COVID-19 vaccine immunogenicity.Cell genomics · 2025Article
- STICI: Split-Transformer with integrated convolutions for genotype imputation.Nature communications · 2025Article
Corrections and comments
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Authors and funding
7 authors at 3 institutions in 1 country.
Funding
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
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.