1. Computational and Systems Biology
  2. Genetics and Genomics

Systematic genetic analysis of the MHC region reveals mechanistic underpinnings of HLA type associations with disease

  1. Matteo D'Antonio
  2. Joaquin Reyna
  3. David Jakubosky
  4. Margaret KR Donovan
  5. Marc-Jan Bonder
  6. Hiroko Matsui
  7. Oliver Stegle
  8. Naoki Nariai
  9. Agnieszka D'Antonio-Chronowska
  10. Kelly A Frazer  Is a corresponding author
  1. University of California, San Diego, United States
  2. European Molecular Biology Laboratory, European Bioinformatics Institute, United Kingdom
https://doi.org/10.7554/eLife.48476
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  1. Matteo D'Antonio
  2. Joaquin Reyna
  3. David Jakubosky
  4. Margaret KR Donovan
  5. Marc-Jan Bonder
  6. Hiroko Matsui
  7. Oliver Stegle
  8. Naoki Nariai
  9. Agnieszka D'Antonio-Chronowska
  10. Kelly A Frazer
(2019)
Systematic genetic analysis of the MHC region reveals mechanistic underpinnings of HLA type associations with disease
eLife 8:e48476.
https://doi.org/10.7554/eLife.48476
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Abstract

The MHC region is highly associated with autoimmune and infectious diseases. Here we conduct an in-depth interrogation of associations between genetic variation, gene expression and disease. We create a comprehensive map of regulatory variation in the MHC region using WGS from 419 individuals to call eight-digit HLA types and RNA-seq data from matched iPSCs. Building on this regulatory map, we explored GWAS signals for 4,083 traits, detecting colocalization for 180 disease loci with eQTLs. We show that eQTL analyses taking HLA type haplotypes into account have substantially greater power compared with only using single variants. We examined the association between the 8.1 ancestral haplotype and delayed colonization in Cystic Fibrosis, postulating that downregulation of RNF5 expression is the likely causal mechanism. Our study provides insights into the genetic architecture of the MHC region and pinpoints disease associations that are due to differential expression of HLA genes and non-HLA genes.

Data availability

Whole-genome sequencing data of 273 individuals in the iPSCORE cohort (Panopoulos et al., 2017) is publicly available through dbGaP: phs001325. RNA-seq data of 215 iPSCs in the iPSCORE cohort (DeBoever et al., 2017) is publicly available through dbGaP:phs000924. Whole-genome sequencing data of 377 individuals in the HipSci cohort is publicly available through EGA: PRJEB15299. RNA-seq data of 231 iPSCs in the HipSci cohort is publicly available through ENA: PRJEB7388. eQTL and HLA-type eQTL summary statistics are available at Figshare (https://figshare.com/s/c8533530204e1822c6f4 and https://figshare.com/s/eab712af0e4baeb99251).

The following previously published data sets were used

Article and author information

Author details

  1. Matteo D'Antonio

    Institute for Genomic Medicine, University of California, San Diego, La Jolla, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-5844-6433

  2. Joaquin Reyna

    Department of Pediatrics and Rady Children's Hospital, University of California, San Diego, La Jolla, United States
    Competing interests
    The authors declare that no competing interests exist.

  3. David Jakubosky

    Biomedical Sciences Graduate Program, University of California, San Diego, La Jolla, United States
    Competing interests
    The authors declare that no competing interests exist.

  4. Margaret KR Donovan

    Bioinformatics and Systems Biology Graduate Program, University of California, San Diego, La Jolla, United States
    Competing interests
    The authors declare that no competing interests exist.

  5. Marc-Jan Bonder

    Wellcome Trust Genome Campus, European Molecular Biology Laboratory, European Bioinformatics Institute, Cambridge, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.

  6. Hiroko Matsui

    Institute for Genomic Medicine, University of California, San Diego, La Jolla, United States
    Competing interests
    The authors declare that no competing interests exist.

  7. Oliver Stegle

    Wellcome Trust Genome Campus, European Molecular Biology Laboratory, European Bioinformatics Institute, Cambridge, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.

  8. Naoki Nariai

    Department of Pediatrics and Rady Children's Hospital, University of California, San Diego, La Jolla, United States
    Competing interests
    The authors declare that no competing interests exist.

  9. Agnieszka D'Antonio-Chronowska

    Institute for Genomic Medicine, University of California, San Diego, La Jolla, United States
    Competing interests
    The authors declare that no competing interests exist.

  10. Kelly A Frazer

    Institute for Genomic Medicine, University of California, San Diego, La Jolla, United States
    For correspondence
    kafrazer@ucsd.edu
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-6060-8902

Funding

California Institute for Regenerative Medicine (GC1R-06673)

  • Kelly A Frazer

National Institutes of Health (HG008118)

  • Kelly A Frazer

National Institutes of Health (HL107442)

  • Kelly A Frazer

National Institutes of Health (DK105541)

  • Kelly A Frazer

National Institutes of Health (DK112155)

  • Kelly A Frazer

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Copyright

© 2019, D'Antonio et al.

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.

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  1. Matteo D'Antonio
  2. Joaquin Reyna
  3. David Jakubosky
  4. Margaret KR Donovan
  5. Marc-Jan Bonder
  6. Hiroko Matsui
  7. Oliver Stegle
  8. Naoki Nariai
  9. Agnieszka D'Antonio-Chronowska
  10. Kelly A Frazer
(2019)
Systematic genetic analysis of the MHC region reveals mechanistic underpinnings of HLA type associations with disease
eLife 8:e48476.
https://doi.org/10.7554/eLife.48476

Share this article

https://doi.org/10.7554/eLife.48476

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