Restricted accessResearch articleFirst published online 2026-4
A bidirectional two-sample Mendelian randomization study of immune responses against Epstein–Barr virus nuclear antigen 1 and multiple sclerosis in individuals of European ancestry
Observational studies suggest an association between immune responses to Epstein–Barr virus nuclear antigen 1 (EBNA1) and multiple sclerosis (MS) risk. Nevertheless, a causal effect cannot be established, as confounding and reverse causation cannot be excluded.
Methods:
We performed a bidirectional two-sample Mendelian randomization (MR) to test whether higher anti-EBNA1 IgG levels causally influence MS risk, or vice versa. Genetic associations for anti-EBNA1 IgG were obtained from 7972 UK Biobank participants, and for MS from 115,803 individuals in the International MS Genetics Consortium. Given that most anti-EBNA1 IgG instruments lie in the pleiotropic major histocompatibility complex, we applied a robust cis-MR framework, sensitivity analyses, and external validation using genetic association from anti-EBNA1 IgG levels obtained from 914 French individuals.
Results:
A one standard deviation increase in genetically predicted anti-EBNA1 IgG levels was causally associated with a 69% higher MS risk (OR = 1.69 [95% CI: 1.28; 2.23], p < 0.001). Results were consistent across sensitivity analyses and showed no directional pleiotropy. External validation supported these findings. Reverse MR provided no evidence that MS influences anti-EBNA1 IgG levels (p > 0.05).
Conclusion:
These results support a unidirectional causal effect of elevated anti-EBNA1 IgG levels on MS risk, reinforcing the role of EBV-related immune responses in MS pathogenesis.
FaziaTBaldrighiGNNovaA, et al. A systematic review of Mendelian randomization studies on multiple sclerosis. Eur J Neurosci2023; 58(4): 3172–3194.
2.
FarrellPJ. EBV and MS: The evidence is growing stronger. Cell2023; 186(26): 5675–5676.
3.
SattarnezhadNKockumIThomasOG, et al. Antibody reactivity against EBNA1 and GlialCAM differentiates multiple sclerosis patients from healthy controls. Proc Natl Acad Sci USA2025; 122(11): e2424986122.
4.
LanzTVBrewerRCHoPP, et al. Clonally expanded B cells in multiple sclerosis bind EBV EBNA1 and GlialCAM. Nature2022; 603(7900): 321–327.
5.
ThomasOGBrongeMTengvallK, et al. Cross-reactive EBNA1 immunity targets alpha-crystallin B and is associated with multiple sclerosis. Sci Adv2023; 9(20): eadg3032.
6.
JogNRMcClainMTHeinlenLD, et al. Epstein Barr virus nuclear antigen 1 (EBNA-1) peptides recognized by adult multiple sclerosis patient sera induce neurologic symptoms in a murine model. J. Autoimmun2020; 106: 102332.
7.
TengvallKHuangJHellströmC, et al. Molecular mimicry between Anoctamin 2 and Epstein-Barr virus nuclear antigen 1 associates with multiple sclerosis risk. Proc Natl Acad Sci USA2019; 116(34): 16955–16960.
8.
SundqvistESundströmPLindénM, et al. Epstein-Barr virus and multiple sclerosis: Interaction with HLA. Genes Immun2012; 13(1): 14–20.
9.
VietzenHBergerSMKühnerLM, et al. Ineffective control of Epstein-Barr-virus-induced autoimmunity increases the risk for multiple sclerosis. Cell2023; 186(26): 5705–5718; e13.
10.
ComabellaMHegenHVillarLM, et al. Increased EBNA1-specific antibody response in primary-progressive multiple sclerosis. J Neurol2025; 272(1): 26.
11.
NovaAGentiliniDDi CaprioG, et al. Stratifying multiple sclerosis susceptibility risk: The role of HLA-E*01 and infectious mononucleosis in a population cohort. Eur J Neurol2025; 32(4): e70131.
12.
VietzenHKühnerLMBergerSM, et al. Early identification of individuals at risk for multiple sclerosis by quantification of EBNA-1381-452-specific antibody titers. Nat Commun2025; 16(1): 6416.
13.
BjornevikKCorteseMHealyBC, et al. Longitudinal analysis reveals high prevalence of Epstein-Barr virus associated with multiple sclerosis. Science2022; 375(6578): 296–301.
14.
HardingKEKreftKLBen-ShlomoY, et al. Prodromal multiple sclerosis: Considerations and future utility. J Neurol2024; 271(4): 2129–2140.
15.
MakhaniNTremlettH. The multiple sclerosis prodrome. Nat Rev Neurol2021; 17(8): 515–521.
16.
BurgessSDavey SmithGDaviesNM, et al. Guidelines for performing Mendelian randomization investigations: Update for summer 2023. Wellcome Open Res2023; 4: 186.
17.
BycroftCFreemanCPetkovaD, et al. The UK Biobank resource with deep phenotyping and genomic data. Nature2018; 562(7726): 203–209.
18.
PatsopoulosNABaranziniSESantanielloA, et al. Multiple sclerosis genomic map implicates peripheral immune cells and microglia in susceptibility. Science2019; 365(6460): eaav7188.
19.
ScepanovicPAlanioCHammerC, et al. Human genetic variants and age are the strongest predictors of humoral immune responses to common pathogens and vaccines. Genome Med2018; 10(1): 59.
20.
Butler-LaporteGKreuzerDNakanishiT, et al. Genetic determinants of antibody-mediated immune responses to infectious diseases agents: A genome-wide and HLA association study. Open Forum Infect Dis2020; 7(11): ofaa450.
21.
KachuriLFrancisSSMorrisonML, et al. The landscape of host genetic factors involved in immune response to common viral infections. Genome Med2020; 12(1): 93.
22.
LinZ. A novel framework with automated horizontal pleiotropy adjustment in mendelian randomization. Hum Genet Genomics Adv2024; 5(4): 100339.
23.
LinZPanW. A robust cis-Mendelian randomization method with application to drug target discovery. Nat Commun2024; 15(1): 1–14.
24.
YangJFerreiraTMorrisAP, et al. Conditional and joint multiple-SNP analysis of GWAS summary statistics identifies additional variants influencing complex traits. Nat Genet2012; 44(4): 369–375.
25.
HemaniGZhengJElsworthB, et al. The MR-base platform supports systematic causal inference across the human phenome. Elife2018; 7: e34408.
26.
LinSHBrownDWMachielaMJ. LDtrait: An online tool for identifying published phenotype associations in linkage disequilibrium. Cancer Res2020; 80(16): 3443–3446.
27.
LinZDengYPanW. Combining the strengths of inverse-variance weighting and Egger regression in Mendelian randomization using a mixture of regressions model. Plos Genet2021; 17(11): e1009922.
28.
ZhuZZhengZZhangF, et al. Causal associations between risk factors and common diseases inferred from GWAS summary data. Nat Commun2018; 9(1): 224.
29.
BarfieldRFengHGusevA, et al. Transcriptome-wide association studies accounting for colocalization using Egger regression. Genet Epidemiol2018; 42(5): 418–433.
30.
HeQBennettANFanB, et al. Assessment of bidirectional relationships between leisure sedentary behaviors and neuropsychiatric disorders: A two-sample Mendelian randomization study. Genes2022; 13(6): 962.
31.
YavorskaOOBurgessS. Mendelian randomization: An R package for performing Mendelian randomization analyses using summarized data. Int J Epidemiol2017; 46(6): 1734–1739.
32.
SandersonERichardsonTGHemaniG, et al. The use of negative control outcomes in Mendelian randomization to detect potential population stratification. Int J Epidemiol2021; 50(4): 1350–1361.
33.
Medina-GomezCMullinBHChesiA, et al. Bone mineral density loci specific to the skull portray potential pleiotropic effects on craniosynostosis. Commun Biol2023; 6(1): 691.
34.
BeaumontRNFlatleyCVaudelM, et al. Genome-wide association study of placental weight identifies distinct and shared genetic influences between placental and fetal growth. Nat Genet2023; 55(11): 1807–1819.
35.
YengoLVedantamSMarouliE, et al. A saturated map of common genetic variants associated with human height. Nature2022; 610(7933): 704–712.
36.
VerbanckMChenCYNealeB, et al. Detection of widespread horizontal pleiotropy in causal relationships inferred from Mendelian randomization between complex traits and diseases. Nat Genet2018; 50(5): 693–698.
37.
DengLZhangHYuK. Power calculation for the general two-sample Mendelian randomization analysis. Genet Epidemiol2020; 44(3): 290.
38.
HuangJTengvallKLimaIB, et al. Genetics of immune response to Epstein-Barr virus: Prospects for multiple sclerosis pathogenesis. Brain2024; 147(10): 3573.
39.
MechelliRUmetonRBellucciG, et al. A disease-specific convergence of host and Epstein-Barr virus genetics in multiple sclerosis. Proc Natl Acad Sci USA2025; 122(14): e2418783122.
