Multiple sclerosis (MS) is an immune-mediated central nervous system disease with mapped genetic risk loci but unresolved cross-layer regulatory mechanisms. We harmonised 30 primary public MS datasets spanning bulk transcriptomics (552 samples, 367 MS/185 healthy control [HC]), DNA methylation arrays (475 samples, 244 MS/231 HC) with sorted-cell whole-genome bisulfite sequencing (WGBS) assessment, single-cell RNA sequencing (RNA-seq; 517,533 cells from 81 donors), and cerebrospinal fluid (CSF) and brain white-matter proteomes, and UK Biobank Pharma Proteomics Project (UK Biobank-PPP) plasma proteomic statistics (407 MS/39,979 HC) for external validation. We prioritised MS-associated genes showing inverse-concordant, cohort-level methylation–expression associations and orthogonal support across layers. After harmonised reprocessing and per-stratum differential testing, a bulk-RNA × methylation inverse-concordance filter followed by an independent proteomic and/or donor-level single-cell anchor identified two Tier-1 candidates (IKZF1’s anchor is a candidate-panel-adjusted donor-level pseudobulk association, not transcriptome-wide significance): ITGB2 and IKZF1. ITGB2 showed the strongest cross-layer support: RNA down-regulation, promoter hypermethylation and reduced plasma abundance in UK Biobank MS cases, a pattern consistent with altered leukocyte-integrin adhesion. Pathway and network analyses linked the candidates predominantly to leukocyte and lymphocyte activation and differentiation, leukocyte-integrin adhesion, and interferon and cytokine-signalling immune modules. These findings nominate MS-relevant regulatory candidates and provide a reusable multi-omics framework for hypothesis generation and validation.

Multiple sclerosis (MS) is a clinically and biologically heterogeneous, immune-mediated disease of the central nervous system, with substantial interindividual variability in disease course and response to disease-modifying therapies (DMTs). Over the past three decades, the MS therapeutic landscape has expanded considerably; however, treatment selection and switching remain guided primarily by clinical phenotype and imaging findings rather than molecular predictors of response. Despite extensive clinical trial evidence, prospectively identifying responders and non-responders to specific DMTs remains challenging. Genetic variability appears to influence differences in treatment efficacy, tolerability, and long-term outcomes in people with MS. Numerous candidate pharmacogenomic variants have been reported across interferon-β, glatiramer acetate, oral agents, and monoclonal antibodies; nevertheless, replication has been inconsistent, effect sizes are modest, and no genetic marker has yet been clinically validated for routine use. Consequently, pharmacogenomics is largely absent from current MS treatment algorithms. This review critically evaluates the existing pharmacogenomic literature across approved DMTs, highlighting reproducible findings, methodological limitations, and gaps that hinder clinical translation. We further discuss requirements for integrating pharmacogenomic markers into routine practice, emphasizing the need for large, multiethnic cohorts, standardized response definitions, and functional validation. Overall, these insights underscore both the potential and current limitations of pharmacogenomics in advancing precision medicine for MS.