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Cited by 418Multiple Sclerosis Research StudiesGenetic Associations and EpidemiologyPsoriasis: Treatment and Pathogenesis

Class II HLA interactions modulate genetic risk for multiple sclerosis

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the International Multiple Sclerosis Genetics Consortium·2015·Nature Genetics
DOI 10.1038/ng.3395PMC PMC4874245
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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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  • Several variants outside the classical regions of the MHC, both class I and class II, were shown to be independently associated, suggesting biological functions beyond antigen display underlie the MHC risk effects.

    “Several variants outside the classical regions of the MHC (both class I and class II) were also shown to be independently associated, suggesting biological functions beyond antigen display underlie the MHC risk effects.”

    From Genome‐wide association studies of multiple sclerosis (2018) · cites this paper as [50]

    The Results section, “A high-resolution map of HLA risk for multiple sclerosis” (page unavailable), reports associated variants near HLA-DPB1 and in LTA, supporting part of the claim about variants beyond classical HLA alleles. It does not establish independently associated nonclassical variants in both class I and class II regions. The Discussion (page unavailable) says the disease mechanisms require functional study, rather than concluding that functions beyond antigen display underlie the risk effects. (LLM, conf=0.84)

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  • The nature of the interaction may be more complicated than expected, with multiple different amino acid-level alleles demonstrating consistent interaction with HLA-DRB1*15:01, in addition to the HLA-DQA1*01:01 allele.

    “However, the nature of the interaction may be more complicated than expected, with multiple different amino acid‐level alleles demonstrating consistent interaction with HLA‐DRB1*15:01, in addition to the HLA‐DQA1*01:01 allele.”

    From Genome‐wide association studies of multiple sclerosis (2018) · cites this paper as [50]

    The Results section, “Interactions among classical HLA alleles” (page not available), reports an interaction involving HLA-DRB1*15:01 and HLA-DQA1*01:01. It says the same interaction can alternatively be explained by HLA-DQB1 alleles encoding glycine at position 70. It does not establish multiple distinct amino-acid-level interactions in addition to the HLA-DQA1*01:01 interaction. (LLM, conf=0.87)

    Experimental — accuracy not yet measured

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Cites

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Research works printed but not linked: 29

  1. [1]Jersild C Svejgaard A Fog T HL-A antigens and multiple sclerosis Lancet 1972 1 1240 1241 4113225
  2. [2]Ligers A Evidence of linkage with HLA-DR in DRB1*15-negative families with multiple sclerosis Am. J. Hum. Genet 2001 69 900 903 11519010 PMC1226077
  3. [4]Kwon OJ HLA class II susceptibility to multiple sclerosis among Ashkenazi and non-Ashkenazi Jews Arch. Neurol 1999 56 555 560 10328250
  4. [6]Baranzini SE Genome-wide association analysis of susceptibility and clinical phenotype in multiple sclerosis Hum. Mol. Genet 2009 18 767 778 19010793 PMC4334814
  5. [7]De Jager PL Meta-analysis of genome scans and replication identify CD6, IRF8 and TNFRSF1A as new multiple sclerosis susceptibility loci Nat. Genet 2009 41 776 782 19525953 PMC2757648
  6. [8]Australia and New Zealand Multiple Sclerosis Genetics Consortium (ANZgene) Genome-wide association study identifies new multiple sclerosis susceptibility loci on chromosomes 12 and 20 Nat. Genet 2009 41 824 828 19525955
  7. [10]Patsopoulos NA Genome-wide meta-analysis identifies novel multiple sclerosis susceptibility loci Ann. Neurol 2011 70 897 912 22190364 PMC3247076
  8. [11]International Multiple Sclerosis Genetics Consortium Analysis of immune-related loci identifies 48 new susceptibility variants for multiple sclerosis Nat. Genet 2013 45 1353 1360 24076602 PMC3832895
  9. [12]Leslie S Donnelly P McVean G A statistical method for predicting classical HLA alleles from SNP data Am. J. Hum. Genet 2008 82 48 56 18179884 PMC2253983
  10. [13]Dilthey A Multi-population classical HLA type imputation PLOS Comput. Biol 2013 9 e1002877 23459081 PMC3572961
  11. [15]Marrosu MG Dissection of the HLA association with multiple sclerosis in the founder isolated population of Sardinia Hum. Mol. Genet 2001 10 2907 2916 11741834
  12. [16]Sanna S Variants within the immunoregulatory CBLB gene are associated with multiple sclerosis Nat. Genet 2010 42 495 497 20453840 PMC3786343
  13. [17]Barcellos LF Heterogeneity at the HLA-DRB1 locus and risk for multiple sclerosis Hum. Mol. Genet 2006 15 2813 2824 16905561
  14. [18]Dyment DA Complex interactions among MHC haplotypes in multiple sclerosis: susceptibility and resistance Hum. Mol. Genet 2005 14 2019 2026 15930013
  15. [19]Marrosu MG Interaction of loci within the HLA region influences multiple sclerosis course in the Sardinian population J. Neurol 2006 253 208 213 16096810
  16. [20]Lincoln MR Epistasis among HLA-DRB1, HLA-DQA1, and HLA-DQB1 loci determines multiple sclerosis susceptibility Proc. Natl. Acad. Sci. USA 2009 106 7542 7547 19380721 PMC2678609
  17. [21]Hedström AK Smoking and two human leukocyte antigen genes interact to increase the risk for multiple sclerosis Brain 2011 134 653 664 21303861
  18. [22]Gregersen JW Functional epistasis on a common MHC haplotype associated with multiple sclerosis Nature 2006 443 574 577 17006452
  19. [23]Evans DM Interaction between ERAP1 and HLA-B27 in ankylosing spondylitis implicates peptide handling in the mechanism for HLA-B27 in disease susceptibility Nat. Genet 2011 43 761 767 21743469 PMC3640413
  20. [24]Strange A A genome-wide association study identifies new psoriasis susceptibility loci and an interaction between HLA-C and ERAP1 Nat. Genet 2010 42 985 990 20953190 PMC3749730
  21. [25]Kirino Y Genome-wide association analysis identifies new susceptibility loci for Behcet’s disease and epistasis between HLA-B*51 and ERAP1 Nat. Genet 2013 45 202 207 23291587 PMC3810947
  22. [26]Dilthey AT Moutsianas L Leslie S McVean G HLA*IMP—an integrated framework for imputing classical HLA alleles from SNP genotypes Bioinformatics 2011 27 968 972 21300701 PMC3065693
  23. [27]Field J A polymorphism in the HLA-DPB1 gene is associated with susceptibility to multiple sclerosis PLoS ONE 2010 5 e13454 21049023 PMC2964313
  24. [28]Yang J Lee SH Goddard ME Visscher PM GCTA: a tool for genome-wide complex trait analysis Am. J. Hum. Genet 2011 88 76 82 21167468 PMC3014363
  25. [30]Masterman T HLA-DR15 is associated with lower age at onset in multiple sclerosis Ann. Neurol 2000 48 211 219 10939572
  26. [31]Goyette P High-density mapping of the MHC identifies a shared role for HLA-DRB1*01:03 in inflammatory bowel diseases and heterozygous advantage in ulcerative colitis Nat. Genet 2015 47 172 179 25559196 PMC4310771
  27. [32]Lang HL A functional and structural basis for TCR cross-reactivity in multiple sclerosis Nat. Immunol 2002 3 940 943 12244309
  28. [34]de Bakker PI A high-resolution HLA and SNP haplotype map for disease association studies in the extended human MHC Nat. Genet 2006 38 1166 1172 16998491 PMC2670196
  29. [36]Roxburgh RH Multiple Sclerosis Severity Score: using disability and disease duration to rate disease severity Neurology 2005 64 1144 1151 15824338

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Authors
the International Multiple Sclerosis Genetics Consortium
Venue
Nature Genetics
Publisher
Springer Science and Business Media LLC
Published
2015-09-07
Volume
47
Issue
10
Pages
1107-1113
ISSN
  • 1061-4036 (ISSN-L)
  • 1546-1718
DOI
10.1038/ng.3395
PMC
PMC4874245
OpenAlex
W2239518951
Cited by
418
References
6
Citations by year
2026: 29 · 2025: 33 · 2024: 46 · 2023: 32 · 2022: 40 · 2021: 42 · 2020: 54 · 2019: 42 · 2018: 46 · 2017: 32 · 2016: 19 · 2015: 2
FWCI
26.21
Type
journal article
Language
en
Open access
Licence
www.springer.com/tdm (tdm)
Locations
  • Nature Genetics (publishedVersion)
  • PubMed (publishedVersion)
  • PubMed Central (acceptedVersion)
  • eCite Digital Repository (University of Tasmania) (submittedVersion)
  • eScholarship (California Digital Library) (submittedVersion)
  • Figshare (submittedVersion, cc-by)
  • Lirias (KU Leuven) (submittedVersion)
  • mediaTUM – the media and publications repository of the Technical University Munich (Technical University Munich) (submittedVersion)
  • Research at the University of Copenhagen (University of Copenhagen) (submittedVersion)
  • PuSH - Publication Server of Helmholtz Zentrum München (submittedVersion)
Funders
  • Wellcome Trust (085475/Z/08/Z, 084702/Z/08/Z, 068545/Z/02), 100956, 085475/B/08/Z, 098051)
  • National Institute of General Medical Sciences (RC2GM093080)
  • National Institute of Neurological Disorders and Stroke (NS26799, RC2NS070340, R25NS070680, NS049477, R01NS032830, R01NS026799, R01NS067305, R01NS049477)
  • National Center for Research Resources (UL1RR024975)
Topics
  • Health Sciences › Medicine › Pathology and Forensic Medicine › Multiple Sclerosis Research Studies
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
  • Life Sciences › Immunology and Microbiology › Immunology › Psoriasis: Treatment and Pathogenesis
Keywords
multiple sclerosis, genetic risk, epistasis, gene-gene interaction, HLA-DRB1*1501, HLA-DQB1*0301, polygenic risk, HLA-A*0201, European ancestry
MeSH terms
Alleles, Histocompatibility Antigens Class II, Epistasis, Genetic, Humans, Multiple Sclerosis, Genetic Predisposition to Disease, Polymorphism, Single Nucleotide

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