From Risk to Trajectory: Can Genetics Predict Disease Course in Multiple Sclerosis?
Multiple sclerosis (MS) is well established as a complex disease with substantial heritable risk, distributed across many common variants rather than driven by a single locus. However, translating genetic discoveries from disease susceptibility into disease course prediction remains a distinct and more difficult scientific problem. Jokubaitis and colleagues review the state of evidence for genetic and epigenetic contributions to MS clinical phenotype and severity, with particular attention to relapse activity and disability progression as the outcomes most relevant to prognostication and precision medicine.
Genetic Architecture: Strong for Risk, Weak for Clinical Phenotype
The review emphasizes an asymmetry: MS risk genetics has advanced rapidly (hundreds of loci identified), while evidence that these loci determine broad clinical phenotype—particularly the distinction between relapsing-onset and progressive-onset disease—remains limited. Large genome-wide studies have not identified robust genetic signatures separating these clinical categories, supporting the interpretation that MS may represent a single disease entity with heterogeneous trajectories rather than genetically distinct disorders. In parallel, familial clustering provides, at best, modest concordance for disease course and does not convincingly support strong heritability for commonly used severity metrics such as the MS Severity Score (MSSS).
Measurement Constraints: Why Progression Genetics Is Hard
A core scientific point in the article is methodological: “severity” in MS is intrinsically challenging to define and measure with high reliability. Disability accumulation is often quantified using the Expanded Disability Status Scale (EDSS) and derivatives (e.g., MSSS), but EDSS is nonlinear, rater-dependent, and heavily weighted toward ambulation. Biomarkers that could standardize progression biology—MRI metrics or neurofilament light chain—either lack universal validation for progression or are strongly confounded by treatment exposure. Consequently, the studies best suited to detect genetic effects on disability progression must be longitudinal, deeply phenotyped, and able to adjust for treatment and environmental variables; these requirements constrain sample size and statistical power relative to conventional GWAS designs.
HLA and Known Risk Variants: Limited Signal for Severity
The strongest individual MS risk association, HLA-DRB1*15:01, is an intuitive candidate for influencing severity, yet large studies show no consistent association with disability progression or clinical phenotype, and only a modest association with earlier age at onset. Extending beyond HLA, multiple approaches that aggregate known susceptibility loci into weighted or cumulative genetic risk scores have generally not demonstrated meaningful association with MSSS or course, after appropriate adjustment for confounders. A notable nuance in the review is the possibility of “functional dichotomy”—that risk and progression could be governed by partly distinct pathways—meaning susceptibility loci may not be the optimal starting point for identifying progression determinants.
A Validated Severity-Related Locus: LRP2 and Relapse Risk
A key advance highlighted is evidence that at least some genetic variation may influence relapse activity. The review describes a genome-wide association signal in LRP2 (rs12988804) associated with relapse risk, reported using longitudinal cohorts and reaching genome-wide significance, and importantly validated in an independent cohort—an essential benchmark given the field’s history of non-replication. Mechanistically, the implicated variant is intronic and not clearly functional, raising the possibility that it tags a rarer causal variant or influences regulation in ways not captured by standard expression/splicing analyses. This example serves as the review’s strongest proof-of-concept that genetics can inform clinically meaningful MS disease activity measures, even if the biology remains to be elucidated.
Beyond GWAS: Exome Sequencing, Candidate Genes, and Expression Studies
The authors assess complementary strategies—exome sequencing, candidate gene testing, and transcriptomics—and conclude that, while biologically plausible signals exist, few are sufficiently replicated to be considered robust. Exome sequencing studies seeking rare, high-impact variants have generated controversial findings (e.g., NR1H3 in familial progressive MS) and have not yielded consistently validated modifiers of progression across cohorts. Candidate gene associations (e.g., adhesion molecules, HSP70, MBP, PD-1 pathway, glucocorticoid receptor haplotypes) may be informative, but the review stresses their vulnerability to type I error and cohort-specific effects. Expression studies and network-based approaches may offer functional context—particularly when integrating GWAS loci with dynamic immune-cell responses—but require careful design, adequate sample sizes, and ideally concurrent protein-level validation to support clinical translation.
Epigenetics and Gene–Environment Interplay: A Plausible Bridge to Severity Biology
An emerging theme is that epigenetic regulation may mediate how environment and aging influence disease course, potentially explaining why purely genetic association studies struggle to account for progression heterogeneity. DNA methylation signatures differ across disease courses in preliminary datasets, though confounding by age and other factors is a persistent concern. The review also notes that exposures such as smoking are associated with worse prognosis and are known to induce measurable methylation changes, suggesting an integrated gene–environment–epigenome framework may be necessary to understand severity. Additionally, genetic variation affecting regulatory molecules such as miRNAs (e.g., miR-146a) offers a mechanistic hypothesis for how inherited variation could shape inflammatory dynamics and relapse propensity.
Disclaimer: This blog post is based on the provided research article and is intended for informational purposes only. It is not intended to provide medical advice. Please consult with a healthcare professional for any health concerns.
References:
Jokubaitis, V.G., Zhou, Y., Butzkueven, H. et al. Genotype and Phenotype in Multiple Sclerosis—Potential for Disease Course Prediction?. Curr Treat Options Neurol 20, 18 (2018). https://doi.org/10.1007/s11940-018-0505-6
