Abstract
The human leukocyte antigen (HLA) locus is associated with more complex diseases than any other locus in the human genome. In many diseases, HLA explains more heritability than all other known loci combined. In silico HLA imputation methods enable rapid and accurate estimation of HLA alleles in the millions of individuals that are already genotyped on microarrays. HLA imputation has been used to define causal variation in autoimmune diseases, such as type I diabetes, and in human immunodeficiency virus infection control. However, there are few guidelines on performing HLA imputation, association testing, and fine mapping. Here, we present a comprehensive tutorial to impute HLA alleles from genotype data. We provide detailed guidance on performing standard quality control measures for input genotyping data and describe options to impute HLA alleles and amino acids either locally or using the web-based Michigan Imputation Server, which hosts a multi-ancestry HLA imputation reference panel. We also offer best practice recommendations to conduct association tests to define the alleles, amino acids, and haplotypes that affect human traits. Along with the pipeline, we provide a step-by-step online guide with scripts and available software (https://github.com/immunogenomics/HLA_analyses_tutorial). This tutorial will be broadly applicable to large-scale genotyping data and will contribute to defining the role of HLA in human diseases across global populations.
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Data availability
We have summarized the availability of HLA imputation reference panels in Table 2. Our HLA imputation pipeline using a multi-ancestry HLA reference panel is publicly available at the MIS (https://imputationserver.sph.umich.edu/index.html).
Code availability
The computational scripts and instructions for their usage related to this tutorial are available at https://github.com/immunogenomics/HLA_analyses_tutorial (https://doi.org/10.5281/zenodo.7373439).
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Acknowledgements
This work is supported in part by funding from the National Institutes of Health (R01AR063759, U01HG012009, UC2AR081023). S.Sakaue was in part supported by the Manabe Scholarship Grant for Allergic and Rheumatic Diseases, the Uehara Memorial Foundation, and the Osamu Hayaishi Memorial Scholarship. J.B.K. was supported by NIH/NIGMS T32GM007753 and NIH/NIAID F30AI172238. A.J.D. was funded by NIH/NIDDK T32DK007028. T.L.L. was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation)—Projektnummer 437857095. Y.O. is supported by AMED (JP22km0405211, JP22km0405217).
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S.Sakaue and S.R. conceived the work and wrote the manuscript with critical input from all authors. S. Sakaue, S.G. and M.C. created a web tutorial accompanying this manuscript. All authors contributed to developing this tutorial. S. Sakaue, M.C., Y.L., W.C., S. Schönherr, L.F., J.L., C.F., Y.O., A.V.S. and S.R. contributed to updating the multi-ancestry HLA reference panel and implementing HLA imputation at the MIS.
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B.H. is a CTO of Genealogy Inc. T.L.L. is a co-inventor on a patent application for using HLA evolutionary divergence in predicting cancer immunotherapy success. S.R. is a founder for Mestag, Inc, a scientific advisor for Sonoma, Jannsen and Pfizer, and serves as a consultant for Sanofi and Abbvie.
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Extended data
Extended Data Fig. 1 The linkage disequilibrium (LD) patterns across the extended MHC region.
A heatmap of LD r2 for pairwise variants across the extended MHC region. We used biallelic markers in our HLA reference panel within European populations and calculated LD r2 values for exhaustive pairs of these variants. The variants are ordered (both on x-axis and y-axis) and annotated by HLA gene names (on x-axis) based on their genomic coordinates on chromosome 6. The bottom plot shows the detailed LD pattern in the class II region.
Extended Data Fig. 2 Schematic illustration of method used to construct scaffold variants within multi-ancestry HLA reference panel.
We extracted SNP variants within MHC region in 1000 Genomes Project (1KG) samples. We only retained variants that were included in major genotyping arrays (Illumina Multi-Ethnic Genotyping Array, Global Screening Array, OmniExpressExome, and Human Core Exome), colored in teal. We then quality controlled each of the participating cohorts’ MHC SNPs separately, retained overlapping variants with selected SNPs in 1KG, and cross-imputed each cohort’s missing variants by using 1KG genotypes. We finally concatenate all cohorts together to construct scaffold variants for multi-ancestry reference panel.
Extended Data Fig. 3 Michigan Imputation Server.
Example usage of Michigan Imputation Server for HLA imputation at https://imputationserver.sph.umich.edu/index.html.
Extended Data Fig. 4 The runtime benchmark for HLA imputation using different platforms.
a. For SNP2HLA, we used BEAGLE4 for phasing and imputation algorithm (Luo et al. Nat Genet. 2021) with using 10 CPUs. For Minmac4, we used SHAPEIT2 as phasing algorithm with samples <10,000 and EAGLE2 as phasing algorithm with samples > 5,000 as we described in the manuscript both with using 10 CPUs. b. For Michigan Imputation Server, we uploaded the unphased genotype data and standard imputation pipeline was performed with default setting (with 1CPU).
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Sakaue, S., Gurajala, S., Curtis, M. et al. Tutorial: a statistical genetics guide to identifying HLA alleles driving complex disease. Nat Protoc 18, 2625–2641 (2023). https://doi.org/10.1038/s41596-023-00853-4
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DOI: https://doi.org/10.1038/s41596-023-00853-4
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