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Significant variation between SNP-based HLA imputations in diverse populations: the last mile is the hardest

The Pharmacogenomics Journal volume 18, pages 367–376 (2018)Cite this article

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Abstract

Four single nucleotide polymorphism (SNP)-based human leukocyte antigen (HLA) imputation methods (e-HLA, HIBAG, HLA*IMP:02 and MAGPrediction) were trained using 1000 Genomes SNP and HLA genotypes and assessed for their ability to accurately impute molecular HLA-A, -B, -C and –DRB1 genotypes in the Human Genome Diversity Project cell panel. Imputation concordance was high (>89%) across all methods for both HLA-A and HLA-C, but HLA-B and HLA-DRB1 proved generally difficult to impute. Overall, <27.8% of subjects were correctly imputed for all HLA loci by any method. Concordance across all loci was not enhanced via the application of confidence thresholds; reliance on confidence scores across methods only led to noticeable improvement (+3.2%) for HLA-DRB1. As the HLA complex is highly relevant to the study of human health and disease, a standardized assessment of SNP-based HLA imputation methods is crucial for advancing genomic research. Considerable room remains for the improvement of HLA-B and especially HLA-DRB1 imputation methods, and no imputation method is as accurate as molecular genotyping. The application of large, ancestrally diverse HLA and SNP reference data sets and multiple imputation methods has the potential to make SNP-based HLA imputation methods a tractable option for determining HLA genotypes.

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Acknowledgements

We thank Janelle Noble, Marc Salit and P Scott Pine for helpful discussions and Abeer Madbouly for assistance with the PCA plots. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH, NIAID, NINDS, NMSS, ONR or United States Government. This work was supported by Office of Naval Research (ONR) grant N00014-08-1-1207 (KB, DP, PAG, JAH, AL, SJM, MM and VP), National Institutes of Health (NIH) grants U01AI067068 (JAH and SJM) and U19AI067152 (ARRA administrative supplement) (PAG) awarded by the National Institute of Allergy and Infectious Diseases (NIAID), R01GM109030 (JAH, SJM and DJP) and P01GM099568 (XZ) awarded by the National Institute of General Medical Sciences (NIGMS), RO1NS076492 (PAG), RO1NS046297 (PAG) and R01NS049477 (PAG) awarded by the National Institute of Neurological Disorders and Stroke (NINDS), and National Multiple Sclerosis Society (NMSS) grant RG 2899-D11 (PAG). PAG is a recipient of the Race to Erase MS Junior Investigator Award and the European Federation for Immunogenetics Julia Bodmer Award. This work was supported by the Australian National Health and Medical Research Council (NHMRC), Career Development Fellowship ID 1053756 (S.L.); and by the Victorian Life Sciences Computation Initiative (VLSCI) grant number VR0240 on its Peak Computing Facility at the University of Melbourne, an initiative of the Victorian Government, Australia (S.L.). Research at the Murdoch Childrens Research Institute was supported by the Victorian Government’s Operational Infrastructure Support Program. We thank President Barack H. Obama for his support and appreciation of American science and basic research.

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Author notes

  1. P-A Gourraud, J A Hollenbach, S J Mack and M Maiers: These authors contributed equally to this work.

Authors and Affiliations

  1. Center for Genetics, Children’s Hospital Research Institute, Oakland, CA, USA

    D J Pappas & S J Mack

  2. Department of Neurology, University of California, San Francisco, CA, USA

    A Lizee, K R Beutner, P-A Gourraud & J A Hollenbach

  3. Bioinformatics Research Department, National Marrow Donor Program, Minneapolis, MN, USA

    V Paunic & M Maiers

  4. Centre for Systems Genomics, Schools of Mathematics and Statistics, and BioSciences, The University of Melbourne, Melbourne, VIC, Australia,

    A Motyer, D Vukcevic & S Leslie

  5. Data Science, Murdoch Childrens Research Institute, Melbourne, VIC, Australia

    A Motyer, D Vukcevic & S Leslie

  6. Department of Biomedical Informatics, Cincinnati Children’s Hospital Medical Center & Department of Environmental Health, University of Cincinnati, Cincinnati, OH, USA

    J Biesiada & J Meller

  7. Los Angeles Biomedical Research Institute, Torrance, CA, USA

    K D Taylor

  8. Department of Biostatistics, University of Washington, Seattle, WA, USA

    X Zheng

  9. Fred Hutchinson Cancer Research Center, Seattle, WA, USA,

    L P Zhao

  10. ATIP-Avenir Team UMR Inserm, 1064, Nantes, France

    P-A Gourraud

  11. Nantes University Hospital Department of Public Health, Nantes, France

    P-A Gourraud

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  1. D J Pappas
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Correspondence to M Maiers.

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Competing interests

SL is a partner in Peptide Groove LLP. Peptide Groove has licensed HLA typing technology to Affymetrix Ltd.

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Pappas, D., Lizee, A., Paunic, V. et al. Significant variation between SNP-based HLA imputations in diverse populations: the last mile is the hardest. Pharmacogenomics J 18, 367–376 (2018). https://doi.org/10.1038/tpj.2017.7

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