Polygenic Risk Scores in Multiple Sclerosis: Advancing Genetic Prediction and Disease Stratification
The article, “Polygenic risk score association with multiple sclerosis susceptibility and phenotype in Europeans,” published in Brain, examines how aggregated genetic variation can refine the assessment of multiple sclerosis susceptibility and disease-related phenotypes. Multiple sclerosis is a chronic inflammatory demyelinating disease of the central nervous system with a substantial but complex genetic component. Rather than being driven by a single mutation, risk is distributed across many loci, each contributing modestly. The authors address this polygenic architecture by developing multiple sclerosis polygenic risk scores (MS-PRS) and evaluating their association with disease status, familial risk, neuroimaging markers, and clinical activity in European-ancestry cohorts.
Methodological Foundation: Building a Robust Polygenic Risk Score
The investigators derived the MS-PRS from International Multiple Sclerosis Genetics Consortium summary statistics comprising 14,802 cases and 26,703 controls, using high-quality HapMap3 variants and the LDPred2 algorithm. This method accounts for linkage disequilibrium and estimates adjusted variant effect sizes, enabling a more refined genetic risk model than earlier approaches based only on genome-wide significant loci. The workflow diagram on page 3 illustrates the study design clearly: discovery statistics were used to derive PRS models, validation occurred in UK Biobank phase 1, and independent testing was performed in UK Biobank phase 2 and Kaiser Permanente Northern California.
Predictive Performance: Risk Stratification Across Cohorts
The MS-PRS showed strong discriminatory performance in independent European cohorts. In UK Biobank phase 2, the score achieved an area under the curve of 0.73, while in the Kaiser Permanente Northern California cohort it reached 0.80, indicating stronger classification in the more clinically curated case-control dataset. The figure on page 5 demonstrates that affected individuals had higher PRS distributions than unaffected individuals in both datasets. Importantly, individuals in the highest PRS strata had markedly increased disease odds: the top decile showed more than a five-fold increased risk in UK Biobank and approximately a fifteen-fold increased risk in Kaiser Permanente relative to the median decile.
Integration With Conventional Risk Factors
A major strength of the study is its evaluation of PRS alongside established non-genetic and clinical risk factors, including sex, age, smoking history, infectious mononucleosis, family history, and body mass index. Adding MS-PRS consistently improved model discrimination beyond conventional risk factors alone. This finding is scientifically important because it indicates that polygenic profiling does not merely duplicate known epidemiological information; rather, it contributes independent risk information. The study also showed that a PRS-based model outperformed a model based only on a single HLA-DRB1*15:01-tagging variant, reinforcing that multiple sclerosis susceptibility is distributed across many genomic regions rather than dominated by one locus.
Biological Interpretation: Pathway-Specific Genetic Risk
The authors extended their analysis by constructing pathway-specific risk scores, thereby moving beyond prediction toward biological interpretation. Several significant pathways were related to adaptive immune regulation, including T-cell receptor signalling, MHC class II antigen presentation, IL-12 signalling, interferon-gamma signalling, and complement pathways. Other implicated pathways involved viral infection response, extracellular matrix organization, cell adhesion, oxidative stress, NOTCH signalling, VEGF signalling, protein glycosylation, and epigenetic regulation. These results align with current mechanistic understanding of multiple sclerosis as an immune-mediated disease, while also highlighting molecular processes that may influence CNS infiltration, inflammatory signalling, and tissue vulnerability.
Familial and Phenotypic Associations
The study also examined PRS in multi-case families, where one parent and at least one child were affected by multiple sclerosis. The familial analysis, shown in the figure on page 8, indicated that affected relatives generally carried higher polygenic burden than unaffected relatives, although predictive accuracy was lower than in population-level analyses because of smaller sample size and relatedness among participants. Beyond susceptibility, the authors investigated associations with disease phenotype. In the UCSF-EPIC cohort, higher genetic predisposition was associated with longitudinal thalamic atrophy, peripheral grey matter volume loss, and relapse activity. The figure on page 9 visualizes this relationship, showing greater thalamic volume loss over time among individuals with higher PRS.
Clinical Implications and Limitations
This article provides evidence that MS-PRS may become a useful component of multifactorial risk models, particularly for individuals with family history, suggestive symptoms, or other established risk factors. However, the findings should not be interpreted as supporting population-wide genetic diagnosis, because multiple sclerosis remains a low-prevalence disease and PRS alone lacks sufficient positive predictive value for universal screening. The study’s principal limitation is ancestry restriction: the models were developed and tested primarily in European-ancestry cohorts, limiting immediate applicability to non-European populations. Overall, the work represents an important translational step, connecting GWAS-derived susceptibility architecture with risk prediction, biological pathway discovery, familial stratification, and MRI-based markers of disease progression.
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:
Shams, H., Shao, X., Santaniello, A., Kirkish, G., Harroud, A., Ma, Q., ... & Oksenberg, J. R. (2023). Polygenic risk score association with multiple sclerosis susceptibility and phenotype in Europeans. Brain, 146(2), 645-656.
