Reworking Genome-Wide Association Studies to Integrate Genetic and Environmental Determinants of Multiple Sclerosis
Multiple sclerosis (MS) is a complex immune-mediated disease of the central nervous system and remains a major cause of neurological disability. Despite substantial therapeutic progress—especially for relapsing–remitting MS—fundamental questions persist about causation and the design of genuinely etiologic therapies. The reviewed article argues that MS should be understood as a complex trait emerging from interactions among genetic predisposition, environmental exposures, and stochastic biological events, and that genome-wide association studies (GWAS) provide a uniquely powerful framework for constructing mechanistic etiologic patterns that can connect inherited risk to modifiable factors.
What GWAS Has Delivered and Why It Is Not Enough
Over the last decade, GWAS have identified more than 200 loci associated with MS susceptibility, revealing a genetic architecture dominated by many common variants of small effect spread across the genome, with notable exceptions in the HLA region and a limited number of rare variants. This polygenicity creates two central bottlenecks highlighted by the authors: first, moving from association signals to causal mechanisms, especially those that integrate nongenetic determinants; and second, translating genomic findings into therapeutic priorities (including repurposing opportunities and discovery of new drug targets). Importantly, the article emphasizes that twin studies and population variability imply a substantial nonheritable contribution, motivating analytic strategies that treat “genetics-only” models as incomplete.
From Single Loci to Function: Learning Mechanisms One Variant at a Time
A key theme is that mechanistic insight often begins with focused “single-gene” follow-ups of GWAS hits. The review summarizes examples where MS-associated variants alter gene regulation or function in ways that illuminate immune pathways: variants in IL2RA can influence autoimmune phenotypes; TYK2 variants can reflect a balance between autoimmunity and immunodeficiency; and IL7R risk can be mediated through epistatic effects on splicing (notably exon 6) that alter soluble receptor biology. The authors also highlight an especially translational example: an MS-associated variant in the TNF receptor region that mirrored the effect of TNF-blocking drugs in increasing demyelinating risk—an instructive case where human genetics can predict adverse therapeutic consequences.
Bioinformatic “Reworking” of GWAS: Pathways, Networks, and Cell Specificity
Because single variants typically explain only a small fraction of risk, the paper surveys analytic approaches that aggregate signals across many loci, sometimes including variants below genome-wide significance thresholds. The review describes pathway analysis and protein–protein interaction (PPI) network strategies that test whether nominally associated loci converge on coherent biological processes. In Table 1 (pages 3–4), studies are contrasted by p-value cutoffs and methods, illustrating how looser thresholds (e.g., p < 0.05) can uncover enriched immune and neural pathways and identify interacting protein subnetworks, while genome-wide significant loci (p < 5 × 10⁻⁸) can be leveraged for transcription factor binding enrichment and cell-specific regulatory inference. More recent systems-biology efforts described in the review prioritize the tissue and cell contexts of MS risk, implicating not only peripheral immune cells but also CNS-resident microglia as important contributors to susceptibility.
Regulatory Genomics and eQTL: A Bridge to Environment and Context
The authors emphasize that many MS-associated variants lie in noncoding regulatory regions, implying that altered gene expression—rather than protein-coding disruption—is a dominant mechanism. In this context, eQTL analyses become essential for connecting genotype to expression changes in relevant cells. The review notes that eQTL effects may be condition- and stimulus-dependent: data derived solely from healthy donors may miss disease-specific regulatory behavior, and some eQTL signals appear stronger in MS patients than in noninflammatory neurological controls. The article also highlights mechanistic work around an MS-associated variant within a microRNA stem-loop sequence in the CD58 locus, proposed to affect Drosha processing and thereby modulate both CD58 and microRNA expression in immune cells—illustrating how GWAS signals can map onto post-transcriptional regulation as well as transcriptional control.
The Candidate-Interactome Concept: Embedding GWAS in Gene–Environment Biology
A distinctive contribution of the review is its detailed discussion of an “interactome-based” framework designed explicitly to incorporate environmental exposures into genome-scale analysis. Here, “interactomes” are defined as sets of human genes whose protein products physically interact with plausible environmental or host factors relevant to MS, with an emphasis on viral interactomes. The logic is to test whether MS-associated variants are enriched near genes whose proteins directly interact with a given exposure, using aggregate statistics rather than single-SNP inference. The authors report that this strategy highlighted Epstein–Barr virus (EBV) as the most prominent MS-associated environmental factor when considered through this lens. The schematic on page 6 (Figure 1) visually summarizes the workflow: genetic predisposition (including SNPs at p < 0.05) is intersected with candidate viral interactomes (e.g., EBV, HIV, HBV), yielding MS-associated interactome genes that are then interrogated through pathway analysis to infer affected cellular functions.
Future Directions: Polygenic Risk Scores and Integrated Etiologic Models
Finally, the review positions polygenic risk scores (PRS) as a promising—though still maturing—tool to quantify cumulative genetic burden and potentially support stratification, prediction, or prevention strategies. The authors propose that PRS in MS could be linked not only to clinical and neuroradiological phenotypes but also to endophenotypes such as radiologically isolated syndrome, and critically, could be extended by incorporating environmental variables and gene–environment interaction terms (G, E, and G×E). They further speculate about integrating host genetic risk with viral genomic variation, particularly EBV variants, to build more comprehensive risk models. While acknowledging major methodological challenges (pleiotropy, causal inference, calibration of interaction effects), the article frames these integrative approaches as part of a broader “post-GWAS era” aimed at moving from statistical association to functional mechanism and actionable intervention.
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:
Mechelli, R., Umeton, R., Manfrè, G., Romano, S., Buscarinu, M. C., Rinaldi, V., ... & Ristori, G. (2020). Reworking GWAS data to understand the role of nongenetic factors in MS etiopathogenesis. Genes, 11(1), 97.
