Skip to main page content
Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2018 Jun 19;9(1):2397.
doi: 10.1038/s41467-018-04732-5.

DNA Methylation as a Mediator of HLA-DRB1*15:01 and a Protective Variant in Multiple Sclerosis

Free PMC article


Lara Kular et al. Nat Commun. .
Free PMC article


The human leukocyte antigen (HLA) haplotype DRB1*15:01 is the major risk factor for multiple sclerosis (MS). Here, we find that DRB1*15:01 is hypomethylated and predominantly expressed in monocytes among carriers of DRB1*15:01. A differentially methylated region (DMR) encompassing HLA-DRB1 exon 2 is particularly affected and displays methylation-sensitive regulatory properties in vitro. Causal inference and Mendelian randomization provide evidence that HLA variants mediate risk for MS via changes in the HLA-DRB1 DMR that modify HLA-DRB1 expression. Meta-analysis of 14,259 cases and 171,347 controls confirms that these variants confer risk from DRB1*15:01 and also identifies a protective variant (rs9267649, p < 3.32 × 10-8, odds ratio = 0.86) after conditioning for all MS-associated variants in the region. rs9267649 is associated with increased DNA methylation at the HLA-DRB1 DMR and reduced expression of HLA-DRB1, suggesting a modulation of the DRB1*15:01 effect. Our integrative approach provides insights into the molecular mechanisms of MS susceptibility and suggests putative therapeutic strategies targeting a methylation-mediated regulation of the major risk gene.

Conflict of interest statement

Sigurgeir Olafsson, Hannes P. Eggertsson, Bjarni V. Halldorsson, Ingileif Jonsdottir and Kari Stefanssonare employees of deCODE genetics/Amgen Inc at the time work related to this study was carried out.H.F. Harbo has received honoraria for advice and lecturing from Biogen, Genzyme, Merck, Novartis,Sanofi-Aventis and Teva. She has received modest unrestricted research grant for research fromNovartis. B. Tackenberg received personal speaker honoraria and consultancy fees as a speaker andadvisor from Bayer Healthcare, Biogen, CSL Behring, GRIFOLS, Merck Serono, Novartis, Octapharma,Roche, Sanofi Genzyme, TEVA und UCB Pharma. His University received unrestricted research grantsfrom Biogen-idec, Novartis, TEVA, Bayer Healthcare, CSL-Behring, GRIFOLS, Octapharma, SanofiGenzyme and UCB Pharma. B. Hemmer has served on scientific advisory boards for F. Hoffmann-LaRoche Ltd, Novartis, Bayer AG, and Genentech; he has served as DMSC member for AllergyCare and TGtherapeutics; he or his institution have received speaker honoraria from Biogen Idec, TevaNeuroscience, Merck Serono, Medimmune, Novartis, Desitin, and F. Hoffmann-La Roche Ltd; hisinstitution has received research support from Chugai Pharmaceuticals and Biogen; holds part of two patents; one for the detection of antibodies against KIR4.1 in a subpopulation of MS patients and one for genetic determinants of neutralizing antibodies to interferon β. The remaining authors declare no competing interests.


Fig. 1
Fig. 1
Study design and workflow diagram. MS: multiple sclerosis, HC: healthy controls, SNP: single nucleotide polymorphism, CIT: causal inference test, eQTL: expression quantitative trait loci, NINDC: non-inflammatory neurological disease controls, SCAND: Scandinavia, SWE: Sweden, DE: Germany, ICE: Iceland
Fig. 2
Fig. 2
DRB1*15:01-associated DNA methylation in monocytes. a Differentially methylated CpGs using 450K arrays between multiple sclerosis (MS, n = 23, red) cases and healthy controls (HC, n = 13, blue) comprising two significant differentially methylated regions (Supplementary Data 2) in the HLA-DRB1 gene. b Differential methylation according to DRB1*15:01 haplotype: homozygous (+/+, n = 4, red), heterozygous (+/−, n = 11, orange), and non-carriers (−/−, n = 20, blue) in MS and HC together (p-values from the additive model). All probes and p-values from other models are shown in Supplementary Data 2. The horizontal gray bars indicate CpGs for which methylation has been validated using other methods (results depicted in ce). c Replication using pyrosequencing of three CpGs according to DRB1*15:01 haplotype: homozygous (+/+, n = 5, red), heterozygous (+/−, n = 17, orange), and non-carriers (−/−, n = 27, blue) (p-values were generated using Kruskal–Wallis test). d Results from Sanger sequencing of bisulfite PCR clones for exon 2 region of HLA-DRB1. Each line represents one read where black and white circles illustrate methylated and unmethylated CpGs, respectively (corresponding probes ID from the 450K array annotation are included). e Methylation of cg06032479 of each allele from individuals heterozygous for DRB1*15:01 (n = 20, red and blue colors representing DRB1*15:01 and the other allele, respectively) quantified by MSRE followed by allele-specific qPCR and tested using the Mann–Whitney test (for n > 3 individuals/group). Individuals’ data are shown in Supplementary Fig. 1. ac Data are presented as Tukey boxplots; *p < 0.05 **p < 0.01, ***p < 0.001
Fig. 3
Fig. 3
DRB1*15:01-associated expression in monocytes. a Spearman correlation between DNA methylation at HLA-DRB1 (exon 2) (450K arrays) and HLA-DRB1 expression quantified by RT-qPCR using primers targeting different segment of the transcript (exon 6 by primer sets 1 and 2, exon 1 by primer set 3, and exon 4-6 by primer set 4). b HLA-DRB1 expression according to DRB1*15:01 haplotype: homozygous (+/+, n = 7, red), heterozygous (+/−, n = 21, orange), and non-carriers (−/−, n = 30, blue) quantified by RT-qPCR (using primer set 1). c HLA-DRB1 expression (n = 4 independent experiments) in PBMCs from DRB1*15:01 non-carriers treated with 5-Aza-2′-deoxycytidine (5-aza-dC). d HLA-DRB1 allele-specific methylation of cg06032474 quantified by MSRE-qPCR from DRB1*15:01 heterozygote (+/−) individuals (n = 20, red and blue colors representing *15:01 and the other allele, respectively). e Relative expression of each HLA-DRB1 allele from DRB1*15:01 heterozygote (+/−) individuals quantified by allele-specific qPCR (n = 26, red and blue colors representing *15:01 and the other allele, respectively). Enhancer (f) and promoter (g) activity of the exon 2 region of DRB1*15:01 using CpG-free promoter-containing (Lucia) and promoter-free (SEAP) reporter gene vectors, respectively. Constructs were partially or fully methylated using HhaI and SssI enzymatic treatment, respectively. Results show relative activity (Lucia or SEAP normalized against Renilla) using five replicates in a representative experiment performed at least 2–3 times. Efficiency of the in vitro methylation is shown in Supplementary Fig. 1f. NA not applicable due to absence of promoter. bg Data are presented as Tukey boxplots; *p < 0.05 **p < 0.01, ***p < 0.001, using Spearman test (a), ANOVA with Dunn’s (b), or Turkey’s multiple comparison tests (c, f, g), the Mann–Whitney test (pooled alleles A vs. pooled alleles B) (d), and t-test (pooled alleles A vs. pooled alleles B) (e)
Fig. 4
Fig. 4
Genotype-dependent candidate DMRs that mediate genetic risk in multiple sclerosis (MS). a Summary workflow and results for identifying epigenetically mediated genetic risk factors for MS. The diagrams on the right represent the relationships between genotype (G), DNA methylation (M), and  MS (phenotype, Y). Dashed lines, the association relationship; arrows, the causal relationship. b Association between candidate genetic risk-mediating DMRs and genotype. Each dashed line represents a potential mediation relationship between an SNP and a DMR as determined by the CIT. c Association between DNA methylation levels at DMR4 chr6:32552039-32552350 (located in exon 2 of HLA-DRB1) that mediates genetic risk in MS and phenotype (top panels), with red and blue colors representing cases and controls, respectively, in blood cells (n = 279) (top left), and in sorted CD14+ monocytes (n = 36), CD19+ B cells (n = 29), CD4+ (n = 33), and CD8+ (n = 29) T cells for cg08578320 (top right), or genotype rs3135338 (bottom left panel) with blue, black, and red colors denoting AA, Aa, and aa genotypes. Bottom right panels: association between genotype (rs3135338) and phenotype and CIT. Red horizontal bars mark percentage of cases for each genotype. Coefficient (β) represents the dependence of the MS phenotype on genotype, with or without adjusting for DNA methylation. The error bars represent the 95% confidence interval for the coefficient β. In the case of the methylation-mediated model, the absolute value of the observed G:Y relationship strength reduces toward zero when adjusting for methylation. DMR: differentially methylated region, SNP: single nucleotide polymorphism, CIT: causal inference test. The full list of SNP-DMR pairs is shown in Supplementary Data 4
Fig. 5
Fig. 5
Two-sample Mendelian randomization (MR). a The top plot shows the effect size from the eQTL analysis (outcome, n = 156, blue) in the PBMC cohort for the SNPs included in the MR. The middle and bottom plots (red) show the effect sizes from the meQTL analysis (exposure, n = 279, red) in the blood cohort for DMR3 and DMR4, respectively, for the SNPs included in the MR. The location of two regions is marked with a dashed line. Further details are given in Methods. b Scatterplots of the effect sizes for meQTL (x-axis) and eQTL (y-axis) for DMR3 (left) and DMR4 (right). The effect size and the 95% confidence intervals are shown. The blue line represents the causal estimate using the Egger regression. All the associations with the exposures were set to be positive and the associations with the outcome were re-oriented
Fig. 6
Fig. 6
Association of methylation-mediated SNPs with multiple sclerosis (MS). a Association between SNPs and MS in the Scandinavian cohort (SCAND, 8172 cases and 13,263 controls) after adjustment for four PCAs (upper panel), the DRB1*15:01 associated terms (middle panel) and all 13 established MS risk variants in the HLA locus (lower panel) (for details see Methods). b Association between SNPs and MS based on meta-analysis of SCAND and three additional cohorts from Sweden (SWE), Germany (DE), and Iceland (ICE) (upper panel, 14,259 cases and 171,347 controls) and Forest plots (lower panel) representing odds ratios (OR, square, proportional to weight) and associated confidence intervals for each cohort and the summary measure (diamond) for the significant (rs9267649) and suggestive (rs2227956) SNPs, with dotted vertical line of no effect. a, b The –log10(p-value) of 47 out of 50 SNPs and their position on chromosome 6 are given on the y- and x-axis, respectively. Colors of circles correspond to different thresholds of statistical significance, red: p-value < 5 × 10−8 (genome-wide significance), orange: 1 × 10−5 < p-value < 5 × 10−8 (suggestive significance) and black: p-value ≥ 1 × 10−5 (non-significant). c Methylation values at DMR3 (exon 2) of HLA-DRB1 gene in DRB1*15:01 heterozygous MS patients and healthy controls (n = 183) stratified according to the rs9267649 (left panel) and rs2227956 (right panel) genotype. d HLA-DRB1 gene expression in DRB1*15:01 heterozygous MS patients and non-MS controls (n = 55) stratified for the rs9267649 (left panel) and rs2227956 (right panel) genotype. cd Significance was estimated using linear regression (for details see Methods)

Similar articles

See all similar articles

Cited by 15 articles

  • DNA Methylation Signature for EZH2 Functionally Classifies Sequence Variants in Three PRC2 Complex Genes.
    Choufani S, Gibson WT, Turinsky AL, Chung BHY, Wang T, Garg K, Vitriolo A, Cohen ASA, Cyrus S, Goodman S, Chater-Diehl E, Brzezinski J, Brudno M, Ming LH, White SM, Lynch SA, Clericuzio C, Temple IK, Flinter F, McConnell V, Cushing T, Bird LM, Splitt M, Kerr B, Scherer SW, Machado J, Imagawa E, Okamoto N, Matsumoto N, Testa G, Iascone M, Tenconi R, Caluseriu O, Mendoza-Londono R, Chitayat D, Cytrynbaum C, Tatton-Brown K, Weksberg R. Choufani S, et al. Am J Hum Genet. 2020 May 7;106(5):596-610. doi: 10.1016/j.ajhg.2020.03.008. Epub 2020 Apr 2. Am J Hum Genet. 2020. PMID: 32243864
  • Evaluation of DNA Methylation Episignatures for Diagnosis and Phenotype Correlations in 42 Mendelian Neurodevelopmental Disorders.
    Aref-Eshghi E, Kerkhof J, Pedro VP; Groupe DI France, Barat-Houari M, Ruiz-Pallares N, Andrau JC, Lacombe D, Van-Gils J, Fergelot P, Dubourg C, Cormier-Daire V, Rondeau S, Lecoquierre F, Saugier-Veber P, Nicolas G, Lesca G, Chatron N, Sanlaville D, Vitobello A, Faivre L, Thauvin-Robinet C, Laumonnier F, Raynaud M, Alders M, Mannens M, Henneman P, Hennekam RC, Velasco G, Francastel C, Ulveling D, Ciolfi A, Pizzi S, Tartaglia M, Heide S, Héron D, Mignot C, Keren B, Whalen S, Afenjar A, Bienvenu T, Campeau PM, Rousseau J, Levy MA, Brick L, Kozenko M, Balci TB, Siu VM, Stuart A, Kadour M, Masters J, Takano K, Kleefstra T, de Leeuw N, Field M, Shaw M, Gecz J, Ainsworth PJ, Lin H, Rodenhiser DI, Friez MJ, Tedder M, Lee JA, DuPont BR, Stevenson RE, Skinner SA, Schwartz CE, Genevieve D, Sadikovic B. Aref-Eshghi E, et al. Am J Hum Genet. 2020 Mar 5;106(3):356-370. doi: 10.1016/j.ajhg.2020.01.019. Epub 2020 Feb 27. Am J Hum Genet. 2020. PMID: 32109418
  • Frameshift mutations at the C-terminus of HIST1H1E result in a specific DNA hypomethylation signature.
    Ciolfi A, Aref-Eshghi E, Pizzi S, Pedace L, Miele E, Kerkhof J, Flex E, Martinelli S, Radio FC, Ruivenkamp CAL, Santen GWE, Bijlsma E, Barge-Schaapveld D, Ounap K, Siu VM, Kooy RF, Dallapiccola B, Sadikovic B, Tartaglia M. Ciolfi A, et al. Clin Epigenetics. 2020 Jan 7;12(1):7. doi: 10.1186/s13148-019-0804-0. Clin Epigenetics. 2020. PMID: 31910894 Free PMC article.
  • Transcribed B lymphocyte genes and multiple sclerosis risk genes are underrepresented in Epstein-Barr Virus hypomethylated regions.
    Ong LTC, Parnell GP, Afrasiabi A, Stewart GJ, Swaminathan S, Booth DR. Ong LTC, et al. Genes Immun. 2020 Feb;21(2):91-99. doi: 10.1038/s41435-019-0089-5. Epub 2019 Oct 16. Genes Immun. 2020. PMID: 31619767 Free PMC article.
  • Environmental and genetic risk factors for MS: an integrated review.
    Waubant E, Lucas R, Mowry E, Graves J, Olsson T, Alfredsson L, Langer-Gould A. Waubant E, et al. Ann Clin Transl Neurol. 2019 Sep;6(9):1905-1922. doi: 10.1002/acn3.50862. Epub 2019 Aug 7. Ann Clin Transl Neurol. 2019. PMID: 31392849 Free PMC article. Review.
See all "Cited by" articles


    1. O’Gorman C, Lin R, Stankovich J, Broadley SA. Modelling genetic susceptibility to multiple sclerosis with family data. Neuroepidemiology. 2013;40:1–12. doi: 10.1159/000341902. - DOI - PubMed
    1. Jersild C, Svejgaard A, Fog T. HL-A antigens and multiple sclerosis. Lancet. 1972;1:1240–1241. doi: 10.1016/S0140-6736(72)90962-2. - DOI - PubMed
    1. Oksenberg JR, et al. Mapping multiple sclerosis susceptibility to the HLA-DR locus in African Americans. Am. J. Hum. Genet. 2004;74:160–167. doi: 10.1086/380997. - DOI - PMC - PubMed
    1. International Multiple Sclerosis Genetics C, et al. Genetic risk and a primary role for cell-mediated immune mechanisms in multiple sclerosis. Nature. 2011;476:214–219. doi: 10.1038/nature10251. - DOI - PMC - PubMed
    1. Moutsianas L, et al. Class II HLA interactions modulate genetic risk for multiple sclerosis. Nat. Genet. 2015;47:1107–1113. doi: 10.1038/ng.3395. - DOI - PMC - PubMed

Publication types