How Genetics and Polygenic Scores Are reshaping Our Understanding of Multiple Sclerosis
Multiple sclerosis (MS) is a prototypical complex autoimmune disorder of the central nervous system, where both genes and environment shape who gets the disease, when it starts, and how it progresses. Family, twin, and adoption studies clearly show that MS aggregates in families, yet no single “MS gene” exists; instead, risk is distributed over hundreds of common variants and a smaller number of rare variants. Recent reviews of MS genetics emphasize that common variants explain only a portion of heritability, but they already provide a rich map of immune pathways—particularly T-cell and B-cell–related biology—that are dysregulated in MS. Polygenic risk scores (PRS) have emerged as a natural way to summarize this highly polygenic architecture into a single quantitative measure of inherited liability to MS, making the genetics more usable in epidemiology and, potentially, clinical practice. [1]
The architecture of MS genetic risk
Among all loci, the major histocompatibility complex (MHC) on chromosome 6 remains the single strongest genetic contributor to MS risk. Alleles of HLA-DRB1 and HLA-DQB1, especially DRB115:01 in individuals of European ancestry, consistently show large effect sizes, and newer work has extended these observations into additional ancestries and clinical sub-phenotypes. Recent studies in North African and Middle Eastern populations, for example, confirm a central role for DRB115:01 while also highlighting population-specific risk and protective HLA alleles, reinforcing the idea that the “MS risk haplotype” is not identical across the globe. [6] Outside the MHC, more than 200 common risk loci have now been identified by large genome-wide association studies (GWAS), implicating cytokine signaling, co-stimulatory pathways, and lymphocyte development. [1,3] Together, these discoveries set the stage for constructing PRS that aggregate thousands of small effects into one risk score.
From GWAS hits to polygenic risk scores
A polygenic risk score is typically computed as a weighted sum of risk alleles, where each variant’s weight comes from GWAS effect sizes (log-odds ratios) and the genotype is coded additively (0/1/2 risk alleles). In MS, early scores used only genome-wide significant variants, but contemporary approaches leverage far denser SNP sets with LD-aware methods (e.g., LD-clumping/thresholding, Bayesian shrinkage, or penalized regression) to optimize prediction. Methodological work across complex diseases shows that PRS performance depends on several factors: discovery sample size, ancestry match between discovery and target cohorts, LD structure, and how carefully confounding and overfitting are controlled. [7] In the MS field, recent conceptual papers have stressed that PRS should not be viewed as deterministic “genetic destiny” but rather as probabilistic modifiers of risk that need to be interpreted in the context of environmental exposures such as Epstein–Barr virus, smoking, vitamin D status, and obesity. [2]
How well do MS PRS predict disease?
Over the last few years, several large studies have evaluated how accurately MS PRS can discriminate cases from controls. A 2024 Nature Communications study constructed a genome-wide MS genetic risk score using more than 200 susceptibility loci and tested it in independent cohorts, showing that individuals in the top tail of the score distribution had substantially higher odds of MS compared with those in the middle or bottom, and that roughly one-fifth of MS heritability could be attributed to common variants captured by the score. [3] Similarly, work in South Asian populations demonstrated that PRS derived from predominantly European GWAS still carried predictive signal but with attenuated performance, underlining the importance of ancestry-matched discovery data and careful transferability analyses. [4] Overall, MS PRS currently achieve good case–control discrimination at the population level but remain far from perfect for individual-level prediction, especially in non-European groups.
Beyond susceptibility: PRS for progression and severity
Initially, most MS PRS research focused on susceptibility—who gets MS. More recently, attention has turned to whether polygenic scores can also capture aspects of disease course, such as progression independent of relapse activity (PIRA), cognitive decline, or need for walking aids. A large Italian cohort study in 2025, for example, evaluated PRS in over 1,100 patients and found that higher genetic risk scores were associated with greater likelihood of PIRA, suggesting that common susceptibility variants may partly overlap with genetic determinants of neurodegeneration and progression. [5] Parallel GWAS of MS severity and age-related disability scores further support the notion that additional loci, sometimes distinct from susceptibility loci, influence how aggressively the disease unfolds. [1,3] These findings point to a future in which multiple PRS—one for susceptibility, one for severity, possibly others for treatment response—may be combined to stratify patients across the entire disease continuum.
Limitations, biases, and ethical questions
Despite the excitement, several limitations temper the immediate clinical deployment of MS PRS. First, the vast majority of discovery data still come from individuals of European ancestry, which leads to weaker performance and potential miscalibration in other ancestral groups, as clearly illustrated by the reduced predictive accuracy of MS PRS in South Asian cohorts. [4] Second, the absolute risk of MS remains low even in high-PRS strata, which means that positive predictive value can be modest unless scores are applied to very high-risk subgroups (e.g., individuals with a family history or strong environmental exposures). [2,3] Third, combining HLA and non-HLA loci raises technical challenges in modeling LD and interactions, especially within the MHC region, which is structurally complex and exhibits extensive allelic diversity. [6] Finally, ethical and psychosocial issues—such as anxiety induced by “high genetic risk” labels, potential insurance discrimination, and consent for secondary use of genomic data—must be addressed explicitly before PRS can be used at scale in asymptomatic individuals. [7]
Future directions for MS genetics and PRS
Looking ahead, the integration of PRS into MS research will likely proceed along several fronts. On the discovery side, larger and more diverse GWAS, along with fine-mapping and proteome-wide association studies, should refine causal variants and genes, which in turn will yield more informative PRS. [1,3] On the translational side, combining PRS with environmental risk scores, imaging biomarkers, and immune phenotyping may enable multi-modal models that outperform genetics alone for predicting conversion from clinically isolated syndrome, stratifying participants in preventive trials, or tailoring surveillance in very high-risk individuals. [2,5,7] Finally, general frameworks for implementing PRS in clinical practice—covering technical validation, reporting standards, and regulatory oversight—are being developed across complex diseases, and MS is well positioned to benefit from these shared infrastructures. [7] While MS PRS are not yet ready for routine screening, they are already powerful tools for mechanistic insight and risk stratification in research, and their clinical relevance is likely to grow as datasets expand and models mature.
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:
[1] Shams H, et al. Polygenic inheritance plays a pivotal role in driving multiple sclerosis susceptibility and phenotype. 2022.
[2] Hone L, et al. Predicting Multiple Sclerosis: Challenges and Opportunities. Frontiers in Neurology, 2022.
[3] Loginovic P, et al. Genetic risk score for multiple sclerosis diagnosis prediction. Nature Communications, 2024.
[4] Breedon JR, et al. Polygenic risk score prediction of multiple sclerosis in individuals of South Asian ancestry. Brain Communications, 2023.
[5] Clarelli F, et al. Contribution of polygenic scores to progression independent of relapse activity in multiple sclerosis. 2025.
[6] Fguirouche A, et al. HLA-DRB1 and DQB1 allelic polymorphism and multiple sclerosis risk in a North African population. 2025.
[7] Hughes J, et al. Polygenic Risk Score Implementation into Clinical Practice: A Review. Genes, 2024.
