Gene–Environment Interactions in Multiple Sclerosis: Evidence from Polygenic Risk
Multiple sclerosis (MS) is a complex immune-mediated disease whose etiology reflects a complex interplay between genetic susceptibility and environmental exposures. While genome-wide association studies (GWAS) have identified hundreds of common genetic variants contributing to MS risk, these variants explain only a portion of overall disease liability. In parallel, epidemiological research has established several environmental and early-life risk factors, including smoking and childhood obesity. The study by Jacobs et al. addresses a critical gap in MS research: whether genome-wide genetic risk modifies the effects of established environmental exposures, thereby contributing to the “missing risk” not explained by genetics or environment alone.
Study Design and Data Sources
The authors leveraged the scale and depth of the UK Biobank, analyzing data from over 488,000 participants, including more than 2,200 individuals with MS. MS cases were identified using a combination of ICD diagnostic codes, general practitioner records, and self-report, while unmatched controls were drawn from the remaining cohort. The large control population provided substantial statistical power to detect modest effects and interactions. Importantly, exposures were selected to reflect early-life or adolescent factors, thereby reducing the likelihood of reverse causation and strengthening causal interpretation.
Environmental Risk Factors Associated with MS
Through multivariable logistic regression, the study confirmed robust associations between MS risk and three environmental factors: increased childhood body size at age 10, smoking before the age of 20, and earlier age at menarche. Childhood obesity emerged as the most consistent and statistically robust exposure, with individuals reporting a “plumper than average” body size showing a significantly increased odds of MS. These findings align with prior observational and Mendelian randomization studies, reinforcing the relevance of early-life metabolic and hormonal factors in MS susceptibility.
Construction and Performance of Polygenic Risk Scores
To quantify genetic susceptibility, the authors developed multiple polygenic risk scores (PRS) using summary statistics from the largest available MS GWAS. Separate scores were constructed including the major histocompatibility complex (MHC) region and excluding it, allowing dissection of MHC-dependent and genome-wide effects. The optimal PRS explained a modest but significant proportion of MS risk and demonstrated good calibration and discriminative ability. Although the variance explained was limited, the monotonic increase in MS risk across PRS deciles validated the utility of these scores as measures of underlying genetic liability.
Evidence for Gene–Environment Interaction
The central finding of the study was a statistically significant additive interaction between polygenic risk and childhood obesity. Individuals with both high genetic risk and childhood overweight status exhibited a greater MS risk than expected from the sum of their independent effects. Notably, this interaction persisted even when the MHC region was excluded from the PRS, providing novel evidence that non-HLA genetic variation contributes meaningfully to gene–environment interplay in MS. In contrast, interactions involving smoking and age at menarche were weaker and did not consistently withstand correction for multiple testing.
Interpretation and Biological Implications
These results suggest that genetic background can amplify the pathogenic impact of certain environmental exposures, particularly childhood obesity. While statistical interaction does not directly equate to biological mechanism, the findings are biologically plausible in light of evidence linking adiposity to immune dysregulation and chronic inflammation. The observation that non-MHC genetic risk modifies environmental effects extends previous work that focused primarily on HLA alleles, underscoring the highly polygenic nature of MS susceptibility.
Implications for Prevention and Future Research
From a translational perspective, this study highlights the potential value of risk stratification in preventive strategies. If individuals with high polygenic risk derive disproportionate benefit from modifying environmental exposures, targeted interventions—such as preventing childhood obesity—may yield greater impact than population-wide approaches alone. Future work will require replication in independent cohorts, extension to more diverse populations, and mechanistic studies to identify the specific pathways underlying these interactions. Nonetheless, this work represents a significant step toward integrating genetic and environmental data in the prevention and risk modeling of multiple sclerosis.
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:
Jacobs, B. M., Noyce, A. J., Bestwick, J., Belete, D., Giovannoni, G., & Dobson, R. (2021). Gene-environment interactions in multiple sclerosis: a UK Biobank study. Neurology: Neuroimmunology & Neuroinflammation, 8(4), e1007.
