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Class II HLA interactions modulate genetic risk for multiple sclerosis

Nature Genetics volume 47, pages 1107–1113 (2015)Cite this article

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Abstract

Association studies have greatly refined the understanding of how variation within the human leukocyte antigen (HLA) genes influences risk of multiple sclerosis. However, the extent to which major effects are modulated by interactions is poorly characterized. We analyzed high-density SNP data on 17,465 cases and 30,385 controls from 11 cohorts of European ancestry, in combination with imputation of classical HLA alleles, to build a high-resolution map of HLA genetic risk and assess the evidence for interactions involving classical HLA alleles. Among new and previously identified class II risk alleles (HLA-DRB1*15:01, HLA-DRB1*13:03, HLA-DRB1*03:01, HLA-DRB1*08:01 and HLA-DQB1*03:02) and class I protective alleles (HLA-A*02:01, HLA-B*44:02, HLA-B*38:01 and HLA-B*55:01), we find evidence for two interactions involving pairs of class II alleles: HLA-DQA1*01:01–HLA-DRB1*15:01 and HLA-DQB1*03:01–HLA-DQB1*03:02. We find no evidence for interactions between classical HLA alleles and non-HLA risk-associated variants and estimate a minimal effect of polygenic epistasis in modulating major risk alleles.

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Figure 1: Overview of genetic effects in the HLA region influencing risk for multiple sclerosis.
Figure 2: Evidence for interactions with associated HLA alleles.
Figure 3: Estimates of polygenic epistasis influencing the effects of major HLA loci on multiple sclerosis.
Figure 4: HLA effects on age at onset.

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Acknowledgements

We thank the individuals, nurses and referring physicians who participated in this study. This project was supported by the US National Institutes of Health (NS049477, NS26799, R01NS032830, RC2NS070340, R01NS067305 and RC2GM093080), the Wellcome Trust (085475/B/08/Z, 085475/Z/08/Z, 084702/Z/08/Z and 098051), the UK MS Society (857/07, 861/07, 862/07, 894/08, 898/08 and 955/11), the UK Medical Research Council (G0700061), Naomi Branson, the Sainsbury Foundation, NMSS (RG 4198-A-1 and 4680-A-1, FG 1938-A-1, JF2138A1, JF-2137A4 and South Florida chapter), the Cambridge National Institute for Health Research (NIHR) British Research Council (BRC), DeNDRon, the Bibbi and Niels Jensens Foundation, the Swedish Brain Foundation, the Swedish Research and County Council, the Knut and Alice Wallenberg Foundation, the Swedish Heart-Lung Foundation, the AFA Foundation, the Foundation for Strategic Research, the Stockholm County Council (592229), the Strategic Cardiovascular and Diabetes Programmes of Karolinska Institutet, the Swedish Council for Working Life and Social Research, INSERM, ARSEP, AFM, GIS-IBISA, BMBF, KKNMS (01GI0917), Deutsche Forschungsgemeinschaft (530/1-1), Munich Biotec Cluster M4, the Fidelity Biosciences Research Initiative, FWO-Vlaanderen, Research Fund KU Leuven (OT/11/087), the Belgian Neurological Society, the Belgian Charcot Foundation, Gemeinnützige Hertie Stiftung, CRPPMS University Zurich, the Danish MS Society, the Danish Council for Strategic Research, the Center of Excellence for Disease Genetics of the Academy of Finland, the Sigrid Juselius Foundation, the Helsinki University Central Hospital Research Foundation, FISM (2011/R/14), Fondazione Cariplo (2010-0728), MIUR (PRIN08), CRT Foundation Turin, the Italian Ministry of Health (502/92), the Italian Foundation for Multiple Sclerosis, INSPE, the Multiple Sclerosis Association of Oslo, the Norwegian Research Council (143153 and 143410), the South-Eastern Norwegian Health Authorities (51852/ILM) and the Australian National Health and Medical Research Council (633275 and 1053756).

We acknowledge the use of samples from the British 1958 Birth Cohort DNA collection (UK Medical Research Council, G0000934 and Wellcome Trust, 068545/Z/02), the UK National Blood Service controls, the Vanderbilt University Medical Center's BioVU DNA Resources Core (US National Institutes of Health, 1UL1RR024975-01), CRB-REFGENSEP, Norwegian Bone Marrow Registry controls, the Norwegian Multiple Sclerosis Registry and Biobank, NARCOMS Registry (CMSC), the Brigham and Women's Hospital PhenoGenetic Project and Academy of Finland DILGOM (136895, 263836 and 118065).

The GWAS made use of external control data from the popgen biobank, Swedish Breast Cancer study, HYPERGENES, CHOP, the Swedish CAD study, BRC-REFGENSEP, Pitié-Salpêtrière CIC, Généthon, the Wellcome Trust Sanger Institute, the University of Miami John P. Hussman Institute, ICM, the Norwegian Multiple Sclerosis Registry and Biobank, the SNP Technology Platform in Uppsala, Sweden, and the University of California, San Francisco. Full acknowledgments are included in the Supplementary Note.

Author information

Author notes

  1. Loukas Moutsianas, Luke Jostins, Ashley H Beecham and Hanne F Harbo: These authors contributed equally to this work.

  2. Stephen Sawcer and Gil McVean: These authors jointly supervised this work.

Authors and Affiliations

  1. Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK

    Loukas Moutsianas, Luke Jostins, Alexander T Dilthey, Dionysia K Xifara, Peter Donnelly, Chris C A Spencer & Gil McVean

  2. John P. Hussman Institute for Human Genomics, University of Miami, Miller School of Medicine, Miami, Florida, USA

    Ashley H Beecham, Margaret A Pericak-Vance & Jacob L McCauley

  3. Department of Clinical Neurosciences, University of Cambridge, Cambridge Biomedical Campus, Cambridge, UK

    Maria Ban, Alastair Compston & Stephen Sawcer

  4. Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, UK

    Tejas S Shah, Carl A Anderson & Jeffrey Barrett

  5. Department of Neurology, Program in Translational NeuroPsychiatric Genomics, Ann Romney Center for Neurologic Diseases, Brigham and Women's Hospital, Boston, Massachusetts, USA

    Nikolaos A Patsopoulos & Philip L De Jager

  6. Department of Medicine, Division of Genetics, Brigham and Women's Hospital, Boston, Massachusetts, USA

    Nikolaos A Patsopoulos & Philip L De Jager

  7. Harvard Medical School, Boston, Massachusetts, USA

    Nikolaos A Patsopoulos & Philip L De Jager

  8. Program in Medical and Population Genetics, Broad Institute of Harvard University and MIT, Cambridge, Massachusetts, USA

    Nikolaos A Patsopoulos, Chris Cotsapas, David A Hafler & Philip L De Jager

  9. Institute of Environmental Medicine (IMM), Karolinska Institutet, Stockholm, Sweden

    Lars Alfredsson

  10. Medical Research Council (MRC) Human Immunology Unit, Weatherall Institute of Molecular Medicine, John Radcliffe Hospital, University of Oxford, Oxford, UK

    Katherine E Attfield & Lars Fugger

  11. Department of Neurology, University of California, San Francisco, Sandler Neurosciences Center, San Francisco, California, USA

    Sergio E Baranzini, Pierre-Antoine Gourraud, Jorge R Oksenberg & Stephen L Hauser

  12. Department of Transfusion Medicine, HLA Laboratory, University Medical Center Hamburg-Eppendorf, Hamburg, Germany

    Thomas M C Binder

  13. Westmead Millennium Institute, University of Sydney, Sydney, New South Wales, Australia

    David Booth & Graeme Stewart

  14. Department of Neurology, Klinikum Rechts der Isar, Technische Universität München, Munich, Germany

    Dorothea Buck, Bernhard Hemmer & Juliane Winkelmann

  15. Department of Neurology, Oslo University Hospital, Ullevål, Oslo, Norway

    Elisabeth G Celius & Hanne F Harbo

  16. Department of Neurology and Immunobiology, Yale University School of Medicine, New Haven, Connecticut, USA

    Chris Cotsapas & David A Hafler

  17. Department of Genetics, Yale University School of Medicine, New Haven, Connecticut, USA

    Chris Cotsapas

  18. Department of Health Sciences, Interdisciplinary Research Center of Autoimmune Diseases (IRCAD), University of Eastern Piedmont, Novara, Italy

    Sandra D'Alfonso

  19. Nuffield Department of Clinical Neurosciences, Weatherall Institute of Molecular Medicine, John Radcliffe Hospital, University of Oxford, Oxford, UK

    Calliope A Dendrou & Lars Fugger

  20. Department of Neurosciences, Laboratory for Neuroimmunology, KU Leuven, Leuven, Belgium

    Bénédicte Dubois & An Goris

  21. Département des Maladies du Système Nerveux and UMRS 1127-7225, INSERM, Université Pierre et Marie Curie, CNRS, Assistance Publique–Hôpitaux de Paris (AP-HP), Institut Cerveau Moelle Spinal Cord and Brain Institute, Pitié-Salpêtrière, Paris, France

    Bertrand Fontaine

  22. Focus Program Translational Neuroscience (FTN), Rhine Main Neuroscience Network (rmn2), Johannes Gutenberg University–Medical Center, Mainz, Germany

    Christiane Graetz, Christina M Lill & Frauke Zipp

  23. Munich Cluster for Systems Neurology (SyNergy), Munich, Germany

    Bernhard Hemmer & Juliane Winkelmann

  24. German Competence Network Multiple Sclerosis (KKNMS), Munich, Germany

    Bernhard Hemmer

  25. Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden

    Jan Hillert, Ingrid Kockum & Tomas Olsson

  26. Murdoch Childrens Research Institute, Royal Children's Hospital, Parkville, Melbourne, Victoria, Australia

    Stephen Leslie

  27. Department of Mathematics and Statistics, University of Melbourne, Parkville, Melbourne, Victoria, Australia

    Stephen Leslie

  28. Platform for Genome Analytics, Institutes of Neurogenetics and Integrative and Experimental Genomics, University of Lübeck, Lübeck, Germany

    Christina M Lill

  29. Division of Neuroscience, Laboratory of Genetics of Neurological Complex Disorders, Institute of Experimental Neurology (INSPE), San Raffaele Scientific Institute, Milan, Italy

    Filippo Martinelli-Boneschi

  30. Department of Neurology, Division of Neuroscience, Institute of Experimental Neurology (INSPE), San Raffaele Scientific Institute, Milan, Italy

    Filippo Martinelli-Boneschi

  31. Department of Neurology, Danish Multiple Sclerosis Center, Copenhagen University Hospital, Copenhagen, Denmark

    Annette Oturai & Helle Bach Søndergaard

  32. Institute for Molecular Medicine Finland, University of Helsinki, Helsinki, Finland

    Janna Saarela

  33. Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway

    Anne Spurkland

  34. Menzies Research Institute Tasmania, University of Tasmania, Hobart, Tasmania, Australia.,

    Bruce Taylor

  35. Institut für Humangenetik, Technische Universität München, Munich, Germany

    Juliane Winkelmann

  36. Institut für Humangenetik, Helmholtz Zentrum München, Munich, Germany

    Juliane Winkelmann

  37. Department of Neurology and Neurological Sciences, Center for Sleep Sciences and Medicine, Stanford University, Stanford, California, USA

    Juliane Winkelmann

  38. Center for Human Genetics Research, Vanderbilt University Medical Center, Nashville, Tennessee, USA

    Jonathan L Haines

  39. Broad Institute of Harvard University and MIT, Cambridge, Massachusetts, USA

    David A Hafler

  40. Harvard NeuroDiscovery Center, Harvard Medical School, Boston, Massachusetts, USA

    Adrian J Ivinson

  41. University of Oslo, Oslo, Norway

    Hanne F Harbo

Consortia

the International Multiple Sclerosis Genetics Consortium

  • Loukas Moutsianas
  • , Luke Jostins
  • , Ashley H Beecham
  • , Alexander T Dilthey
  • , Dionysia K Xifara
  • , Maria Ban
  • , Tejas S Shah
  • , Nikolaos A Patsopoulos
  • , Lars Alfredsson
  • , Carl A Anderson
  • , Katherine E Attfield
  • , Sergio E Baranzini
  • , Jeffrey Barrett
  • , Thomas M C Binder
  • , David Booth
  • , Dorothea Buck
  • , Elisabeth G Celius
  • , Chris Cotsapas
  • , Sandra D'Alfonso
  • , Calliope A Dendrou
  • , Peter Donnelly
  • , Bénédicte Dubois
  • , Bertrand Fontaine
  • , Lars Fugger
  • , An Goris
  • , Pierre-Antoine Gourraud
  • , Christiane Graetz
  • , Bernhard Hemmer
  • , Jan Hillert
  • ,  International IBD Genetics Consortium (IIBDGC)
  • , Ingrid Kockum
  • , Stephen Leslie
  • , Christina M Lill
  • , Filippo Martinelli-Boneschi
  • , Jorge R Oksenberg
  • , Tomas Olsson
  • , Annette Oturai
  • , Janna Saarela
  • , Helle Bach Søndergaard
  • , Anne Spurkland
  • , Bruce Taylor
  • , Juliane Winkelmann
  • , Frauke Zipp
  • , Jonathan L Haines
  • , Margaret A Pericak-Vance
  • , Chris C A Spencer
  • , Graeme Stewart
  • , David A Hafler
  • , Adrian J Ivinson
  • , Hanne F Harbo
  • , Stephen L Hauser
  • , Philip L De Jager
  • , Alastair Compston
  • , Jacob L McCauley
  • , Stephen Sawcer
  •  & Gil McVean

Contributions

A.C., A.O., A.S., B.D., B.F., B.H., B.T., C.G., C.M.L., D.A.H., D. Booth, D. Buck, E.G.C., F.M.-B., F.Z., G.S., H.F.H., I.K., IIBDGC, J.H., J.L.H., J.L.M., J.R.O., J.S., L.A., P.L.D.J., S.D'A., S.L.H., S.S., T.M.C.B. and T.O. were involved with case ascertainment and phenotyping. A.G., A.O., B.F., D. Booth, D. Buck, F.M.-B., H.B.S., H.F.H., I.K., J.L.M., J.R.O., M.B., P.L.D.J., S.D'A. and S.S. processed the DNA. A.O., B.F., D.A.H., H.B.S., IIBDGC, J.L.M., M.B., P.L.D.J. and S.S. conducted and supervised the genotyping of samples. A.H.B., A.T.D., C.A.A., C.C.A.S., C.C., D.K.X., G.M., J.L.H., J.L.M., L.F., L.J., L.M., M.B., N.A.P., P.D., P.L.D.J., P.-A.G., S.E.B., S.L., S.S. and T.S.S. performed the analysis. A.G., A.H.B., A.J.I., A.O., A.T.D., B.D., B.F., B.H., C.A.A., C.C.A.S., C.C., C.G., C.M.L., D. Booth, D. Buck, D.K.X., E.G.C., F.M.-B., F.Z., G.M., H.B.S., H.F.H., I.K., J.B., J.L.H., J.L.M., J.S., J.W., L.J., L.M., M.B., N.A.P., P.D., P.L.D.J., S.D'A., S.L., S.S., T.M.C.B. and T.S.S. collected and managed the project data. A.C., A.H.B., A.J.I., A.S., B.H., C.A.A., C.A.D., C.C.A.S., C.C., C.G., C.M.L., D.A.H., E.G.C., F.M.-B., F.Z., G.M., G.S., H.F.H., J.B., J.H., J.L.H., J.L.M., J.R.O., J.S., J.W., K.E.A., L.A., L.J., M.A.P.-V., M.B., N.A.P., P.D., P.L.D.J., S.D'A., S.L., S.L.H., S.S., T.M.C.B., T.O. and T.S.S. contributed to the study concept and design. All authors reviewed and approved the final manuscript.

Corresponding author

Correspondence to Gil McVean.

Ethics declarations

Competing interests

A.T.D., P.D., S.L. and G.M. are partners in Peptide Groove, LLP, which commercializes HLA*IMP.

Integrated supplementary information

Supplementary Figure 1 Effects of incorporating uncertainty in classical HLA allele prediction.

Comparison of P values and odds ratios estimated for each cohort for all classical HLA allele effects shown in Figure 1 under methods taking account of uncertainty in classical HLA allele imputation (y axis) and not taking into account uncertainty (x axis). The values reported are from a model including additive and non-additive effects for DRB1*15:01 and principal components, in addition to the indicated allele. DOM, dominant term; REC, recessive term; INT, interaction term.

Supplementary Figure 2 Comparison of manually curated and automated approaches for building a model of HLA risk for multiple sclerosis.

Summary of the results from the model selection procedures (without interaction search) using manual curation (left), automatic model search with alleles at four-digit resolution (middle) and automatic mega-analysis augmented with alleles grouped at two-digit resolution and by sharing of amino acid residues. Alleles are shown in order of inclusion in each approach and were matched between approaches by LD (the Pearson correlation coefficient across the entire cohort is shown and groups are identified by color). At each stage, models with additive (no superscript), dominant (D), recessive (R) and general (G) risk were tested. The allele used to refer to each group is indicated in bold. Effects identified that were better explained by interactions between other alleles are underlined. Note that DRB1_AA57.S is a near-perfect proxy (r = 0.95) for the presence of either DRB1*08:01 or DRB1*13:03, although a model with independent effects for the two alleles provides better fit. We note also that B_AA326.C is weakly correlated with the presence of B*44:02 (r = 0.26), although the latter remains significant in FEM if the former is included.

Supplementary Figure 3 Evidence for interactions among classical HLA alleles affecting risk for multiple sclerosis.

Quantile-quantile plots showing the distribution of P values in the UK cohort for the interaction term between HLA alleles reported in the full model and other classical HLA alleles. For each test, the full model (i.e., factors shown in Fig. 1 apart from interactions), including non-additive effects for DRB*15:01, DRB1*03:01 and A*02:01, is fitted, along with the potential interaction. Results are shown for the UK cohort only. Note that DPB1*03:01 is shown here rather than the related SNP rs9277565.

Supplementary Figure 4 Evidence for interactions between classical HLA alleles and non-HLA disease-associated variants affecting risk for multiple sclerosis.

Quantile-quantile plots showing the distribution of P values within the UK cohort for the interaction term between HLA alleles reported in the full model and non-HLA SNP variation influencing genetic risk for multiple sclerosis (from ref. 11). For each test, the full model, including non-additive effects for DRB*15:01, DRB1*03:01 and A*02:01, is fitted, along with the potential interaction. Results are shown for the UK cohort only. Note that DPB1*03:01 is shown here rather than the related SNP rs9277565.

Supplementary Figure 5 Interactions between HLA variants and combined non-HLA risk for multiple sclerosis.

The effect of the indicated allele or variant in the UK cohort among individuals stratified into quartiles by a combined non-HLA risk score, obtained by multiplying the odds ratios associated with each genotype carried by an individual at non-HLA loci influencing risk (from ref. 11). The point estimate and 95% confidence interval for effect size are estimated independently for each quartile of non-HLA genetic risk. Dashed and dotted lines indicate the combined point estimate and 95% confidence interval, respectively. HOM, risk for homozygous genotype; DOM, dominance term; INT, interaction term.

Supplementary Figure 6 Conflation between evidence for interactions and a departure from additivity for DRB1*15:01 and A*02:01.

Top, quantile-quantile plots showing the distribution of P values for interaction terms between DRB1*15:01 (left) and A*02:01 (right) and non-HLA variants known to affect risk of multiple sclerosis (from ref.11) under a model where the HLA allele acts additively (on the log-odds scale). Bottom, as above, but where a departure from additivity is included for DRB1*15:01 and A*02:01. Because potential interactions are modeled as an additional additive effect of the associated SNP that only acts in the presence of at least one copy of the HLA allele, deviations from additivity can partly mimic potential interactions with the non-HLA variants.

Supplementary Figure 7 Classical HLA alleles affecting age at onset.

Meta-analysis showing effects of pairs of classical HLA alleles in strong LD (DRB1*15:01–DQB1*06:02 and DRB1*01:01–DQA1*01:01) on age at onset.

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Supplementary Figures 1–7, Supplementary Tables 1–5 and Supplementary Note. (PDF 2332 kb)

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the International Multiple Sclerosis Genetics Consortium. Class II HLA interactions modulate genetic risk for multiple sclerosis. Nat Genet 47, 1107–1113 (2015). https://doi.org/10.1038/ng.3395

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