Genetics

This blog post summarizes a Nature Communications study that tackles the central post-GWAS challenge in multiple sclerosis (MS): translating predominantly non-coding genetic associations into mechanistic, cell-type–resolved biological insight. The article introduces a quantitative “predicted regulatory effect” (PRE) approach to prioritize target genes at MS risk loci by integrating linkage disequilibrium structure with large-scale functional genomic annotations, then evaluates whether implicated genes converge into coherent protein–protein interaction modules across immune compartments. It further tests molecular plausibility by relating individualized PRE estimates to gene expression in primary immune cells and extends the framework to patient-level risk networks that capture heterogeneity in cell-specific genetic burden. Collectively, the work demonstrates how regulatory annotation and interactome topology can bridge population-scale association signals to actionable hypotheses about immune pathways and inter-individual variability in MS susceptibility.