Part IV

Genetics

HLA-DRB1*15:01 dominates a polygenic landscape of more than 230 immune-system loci. The familial, twin and GWAS data fit a model of common-variant, immune-driven susceptibility — with environment (EBV, vitamin D, smoking) converting genetic risk into disease.

1. Family & Twin Studies — Heritability of MS

MS aggregates in families and behaves as a complex trait of moderate heritability:

RelationshipLifetime riskRecurrence vs population
General population (NW Europe)~0.3%
First-degree relative~3%~10×
Sibling~3–5%~10–20×
Dizygotic twin~5%~20×
Monozygotic twin~25–30%~80–100×
Child of two MS parents~30%~100×

The discordance between MZ (~30%) and DZ (~5%) twin concordance gives a narrow-sense heritability estimate of \(h^2 \approx 0.5\) on the liability scale. The fact that MZ concordance is well below 100% establishes that environment is essential — consistent with the EBV picture from Part III.

2. HLA-DRB1*15:01 — the Major Susceptibility Allele

The MHC association with MS, first described by Jersild (1972), is the strongest in any neurological disease. Fine-mapping has placed the dominant signal on the HLA-DRB1*15:01 allele (within the DR2 haplotype DRB1*15:01-DQB1*06:02):

  • One copy — OR ~3 for MS in northern European populations.
  • Two copies (homozygote) — OR ~6 (additive on the log-OR scale).
  • HLA-DRB1*15:01 carriage explains roughly 10–20% of MS heritability on the liability scale.
  • Mechanistically, DR2 presents myelin peptides (notably MBP85-99) with high affinity to autoreactive CD4+ T cells — the structural basis is captured in PDB 1IEA (shown in Part III).

The same allele is also strongly associated with other autoimmune diseases (narcolepsy, type-1 diabetes), arguing for a generic effect of altered antigen presentation rather than an MS-specific mechanism.

3. Other HLA Class-II and Class-I Effects

Beyond DRB1*15:01, the MHC harbours independent susceptibility and protective signals:

  • HLA-DRB1*03:01, *13:03, *08:01 — secondary risk alleles.
  • HLA-A*02:01 — a class-I protective allele (OR ~0.6); presents EBV-derived peptides and may aid CD8+ control of EBV.
  • HLA-B*44:02 — class-I protective.
  • Epistatic interaction between DRB1*15:01 and A*02:01 has been described — the protective effect of A*02:01 is greatest in DRB1*15:01 carriers.

The net MHC effect on MS risk requires modelling all class-I and class-II alleles jointly. Modern imputation panels (HLA*IMP:02, SNP2HLA) make this routine in GWAS.

4. The IMSGC GWAS — 233 Non-MHC Loci

The International Multiple Sclerosis Genetics Consortium (IMSGC) has progressively scaled MS GWAS:

  • 2007 — first MS GWAS (931 trios), confirms IL-7R, IL-2RA.
  • 2011 — IMSGC + WTCCC2 (Nature 2011): 9772 cases, 17,376 controls; 57 non-MHC loci.
  • 2013 — ImmunoChip analysis (47 638 cases + ctrls): 110 non-MHC loci.
  • 2019 — IMSGC (Science 2019): 47,429 cases, 68,374 controls, 233 non-MHC genome-wide-significant loci; 32 independent MHC effects.
  • 2023 — IMSGC (Nature 2023): identification of a non-coding variant near DYSF/ZNF638 associated with severity (independent of susceptibility).

The architecture is profoundly immune-system-centric: enrichment analyses show non-MHC associations cluster in CD4+ T cells (Th1/Th17), B cells, and dendritic cells. No locus implicates an oligodendrocyte- or neuron-intrinsic gene at genome-wide significance — consistent with MS being primarily an immune disease, the CNS being the target rather than the source of the defect.

5. Cytokine-Receptor and Co-Stimulation Genes

Several non-MHC loci have well-mapped functional consequences:

GenePathwayFunctional effect
IL-7R (CD127)T-cell homeostasisRisk allele alters splicing → ↑ soluble IL-7R, ↑ inflammatory T-cell signalling
IL-2RA (CD25)Treg / IL-2 axisRisk allele ↓ Treg function (target of daclizumab)
TNFRSF1ATNF receptor 1R92Q variant produces decoy receptor; explains paradoxical worsening of MS by anti-TNF agents
CD58T-cell co-stimRisk allele lowers CD58 → reduced Treg suppression
TYK2JAK/STATLoss-of-function variant protective; implicates IL-12/23 pathway
CD40B-T crosstalkRisk allele alters B-cell activation
EOMES, RGS1, STAT4Th1/Th17 differentiationMultiple risk alleles tune effector T-cell programmes
CYP27B1, CYP24A1Vitamin D metabolismMendelian randomisation supports causal vitamin D effect

Several loci overlap with other autoimmune diseases (rheumatoid arthritis, type-1 diabetes, IBD); shared pathways include IL-2/IL-7 signalling, TYK2/JAK-STAT, and antigen presentation. MS-specific effects concentrate in T- and B-cell programmes rather than innate-immunity genes.

6. Polygenic Risk Score

An MS polygenic risk score (PRS), summing effect-weighted genotypes across >200 loci, has area-under-curve of ~0.65–0.70 for case-control discrimination. Top decile of PRS confers ~5× increased risk vs lowest decile. PRS does not currently warrant clinical screening, but is increasingly used:

  • To enrich populations for prevention trials (EBV vaccine).
  • To stratify CIS patients for conversion risk.
  • In pleiotropy analyses across autoimmune diseases.

Formally, on the liability scale, the proportion of variance explained by the PRS is \(R^2 \approx 0.20\); HLA-DRB1*15:01 alone contributes ~10%; the rest is split among 233+ loci of individually small effect (typical OR 1.05–1.20).

7. The Heritability Gap

Twin studies place MS heritability at \(h^2 \approx 0.5\). The cumulative variance explained by all GWAS-significant loci, even when the MHC is fully accounted for, is closer to ~0.20. The missing heritability may reflect:

  • Rare variants of larger effect not captured by SNP arrays (whole-exome / whole-genome sequencing initiatives are underway).
  • Structural variants in the MHC (DRB1 copy-number, DR/DQ haplotype effects).
  • Gene-gene interactions (epistasis), particularly within MHC.
  • Gene-environment interactions absorbing apparent heritability under standard models.
  • Twin concordance estimates that may overstate \(h^2\) (shared environments).

The gap is comparable to that observed in other complex autoimmune diseases and is not specific to MS.

8. Gene–Environment Interactions

The most replicated gene-environment effect in MS is between HLA-DRB1*15:01 and smoking (and infectious mononucleosis):

  • HLA-DRB1*15:01 carriage alone — OR ~3.
  • HLA-DRB1*15:01 + smoking — OR ~14 (multiplicative interaction; Hedstrom et al., Brain 2011).
  • HLA-DRB1*15:01 + IM history — OR ~9.
  • HLA-DRB1*15:01 + adolescent obesity — OR ~6 in women.

These interactions reframe MS prevention as a gene-targeted environmental intervention: the same exposure (smoking, EBV, low vitamin D) yields very different population effects depending on HLA background.

9. Pharmacogenomics & Severity Genetics

MS pharmacogenomics is younger than its susceptibility genetics, but several signals are clinically actionable:

  • JCV serology — risk of progressive multifocal leukoencephalopathy on natalizumab is stratified primarily by JC virus antibody status (clinical, not genetic).
  • Glatiramer acetate response — HLA-DRB1*15:01 carriers may respond differently; data conflicting.
  • Interferon-β neutralising antibodies — HLA-DRB1*15:01 and *07:01 affect rates.
  • DYSF/ZNF638 locus — the IMSGC 2023 paper identified a non-coding variant associated with disease severity (time to EDSS 6) but not susceptibility — the first severity locus.
  • CYP27B1, CYP24A1, GC — vitamin D pathway genotypes modulate response to vitamin D supplementation.

The dissociation of susceptibility (immune) and severity (potentially CNS-intrinsic) genetics is a key 2020s finding: genes that determine whether someone develops MS may differ from those that determine how badly they progress.

Key references for further reading. International Multiple Sclerosis Genetics Consortium, Multiple sclerosis genomic map, Science 2019; IMSGC, Locus for severity implicates CNS resilience, Nature 2023; Sawcer et al., Genetic risk and a primary role for cell-mediated immune mechanisms in MS, Nature 2011; Hedstrom et al., Smoking and HLA-DRB1*15 interaction, Brain 2011; Mokry et al., Vitamin D and MS — Mendelian randomization, PLoS Med 2015.
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