Gene–Environment Interactions in Multiple Sclerosis: Insights from UK Biobank
The article “Gene-environment interactions in Multiple Sclerosis: a UK Biobank study” investigates how inherited genetic susceptibility may modify the influence of environmental risk factors on the development of Multiple Sclerosis (MS). MS is presented as a complex neuro-inflammatory disease shaped by both genetic architecture and environmental exposures, rather than by a single causal pathway. Using the UK Biobank cohort, the authors aimed to determine whether polygenic risk scores for MS interact with established environmental risk factors, particularly those occurring in childhood or adolescence.
Scientific Rationale: Beyond Single Risk Factors
The study is motivated by the observation that although genome-wide association studies have identified many MS-associated loci, a substantial proportion of disease susceptibility remains unexplained. Prior research has shown that environmental exposures such as smoking, childhood obesity, infectious mononucleosis, vitamin D deficiency, and latitude are associated with MS risk. However, most earlier gene-environment studies focused on the HLA region, especially alleles such as HLA-DRB1*15:01 and HLA-A*02:01. This article extends the question beyond HLA by asking whether genome-wide polygenic risk also modifies environmental effects.
Study Design and Data Sources
The authors used UK Biobank data, identifying MS cases through ICD-coded diagnoses, self-report, or general practitioner records. The study included more than 2,200 individuals with MS and approximately 486,000 controls, providing substantial statistical power for a case-control analysis. Environmental exposures examined included childhood body size at age 10, smoking before age 20, age at menarche, birth weight, month of birth, breastfeeding history, maternal smoking exposure, infectious mononucleosis before age 20, and puberty-related variables. The investigators adjusted their models for major potential confounders including age, sex, ethnicity, birth latitude, and deprivation status.
Polygenic Risk Scores and Genetic Stratification
A central methodological feature of the article is the development of polygenic risk scores (PRS) for MS using summary statistics from the International Multiple Sclerosis Genetics Consortium. The authors created PRS models both including the Major Histocompatibility Complex region and excluding it, allowing them to separate the contribution of the dominant HLA region from broader genome-wide genetic burden. The best-performing PRS models were selected in a training subset and then validated in a separate testing subset. Both the MHC-inclusive and non-MHC PRS were strongly associated with MS, although the MHC-inclusive model showed greater discriminatory performance.
Key Findings: Childhood Obesity as a Gene-Environment Interaction
The most important result of the study was evidence for an interaction between childhood body size and genetic risk. Individuals who reported being “plumper” at age 10 had an increased risk of MS, and this association was stronger among individuals with higher polygenic susceptibility. Crucially, the interaction remained evident even when the MHC region was excluded from the polygenic score, suggesting that genetic modulation of childhood obesity-related MS risk is not limited to classical HLA biology. The authors quantified this interaction using the attributable proportion due to interaction, reporting significant additive interaction for both MHC and non-MHC PRS models.
Additional Findings and Biological Interpretation
The study also found that smoking before age 20 and earlier age at menarche were associated with MS risk in the UK Biobank cohort. However, the strongest and most consistent gene-environment interaction signal involved childhood body size. The biological interpretation is that obesity-related immune, inflammatory, hormonal, or metabolic pathways may amplify underlying genetic susceptibility to autoimmunity. The article also reports evidence that non-MHC polygenic risk may interact with HLA-DRB1*15:01, suggesting that the effect of a major MS risk allele may itself depend on broader genetic background.
Limitations and Implications for Prevention
The authors appropriately emphasize that statistical interaction does not necessarily prove direct biological interaction. The study is also limited by retrospective self-reported exposures, imperfect case ascertainment, under-reporting of some exposures such as infectious mononucleosis, and the predominantly White British composition of UK Biobank. Nevertheless, the findings have important implications for MS prevention research. They suggest that interventions targeting childhood obesity may have the greatest preventive value among genetically susceptible individuals, and that future MS risk prediction models may need to integrate environmental exposure history with genome-wide genetic risk.
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., Bestwick, J., Belete, D., Giovannoni, G., & Dobson, R. (2020). Gene-environment interactions in Multiple Sclerosis: a UK Biobank study. bioRxiv, 2020-03.
