Multiancestry GWAS and multiomics reveal cellular origins of multiple sclerosis genetics

A landmark international study has delivered the most comprehensive picture to date of the genetic architecture of multiple sclerosis, combining genome-wide association data from individuals across multiple ancestries with cutting-edge multiomics analyses to reveal where, and in which cell types, the disease’s genetic risk exerts its effects. The research, published in Nature Genetics, represents a significant step forward in resolving one of the most persistent puzzles in complex disease genetics: why risk variants identified in primarily European-ancestry populations fail to explain disease susceptibility in other populations, and how thousands of statistically associated genetic variants translate into biological dysfunction within specific cells of the central nervous and immune systems.

Multiple sclerosis is a chronic, immune-mediated demyelinating disease of the central nervous system, affecting nearly three million people worldwide. It is characterized by immune cell infiltration into the brain and spinal cord, destruction of the myelin sheaths that insulate nerve fibers, and progressive neurodegeneration. Decades of family and twin studies have established that genetics contributes substantially to disease risk, with heritability estimates ranging between 25 and 50 percent. The strongest single genetic signal lies within the human leukocyte antigen (HLA) region on chromosome 6, particularly the HLA-DRB1*15:01 allele, which confers a roughly threefold increase in risk. Beyond HLA, however, more than 200 non-HLA risk loci have been identified, each contributing only modest effects. Until now, nearly all of these discoveries have come from cohorts overwhelmingly composed of individuals of European ancestry, limiting both the precision and the portability of the resulting biological insights.

The new study tackled this limitation head-on through a multiancestry genome-wide association study (GWAS) of unprecedented scale. By pooling genetic and clinical data from tens of thousands of individuals with multiple sclerosis and comparable numbers of unaffected controls drawn from European, East Asian, African, Hispanic and Latin American, and other ancestry groups, the consortium was able to boost statistical power well beyond what any single-ancestry cohort could achieve. Combining ancestries in a single analysis increases the effective sample size, while trans-ancestry comparisons exploit differences in linkage disequilibrium patterns—the nonrandom association of variants across populations—to fine-map disease associations more precisely. When the same haplotype block is inherited differently across ancestries, the causal variant can be pinpointed by looking for the signal that remains consistent while surrounding markers shift.

This fine-mapping strategy allowed the researchers to narrow the credible sets of candidate causal variants at many loci, in some cases reducing lists of dozens of plausible candidates to just a handful. The team also identified novel risk loci that had gone undetected in European-only studies and demonstrated that some previously reported associations were population-specific, driven by alleles common in one ancestry but rare or absent in others. Several of these ancestry-specific signals were found in non-Europeans for the first time, underscoring the importance of diversity in genetic research and the risk of systematically overlooking disease biology in underrepresented populations. The consortium also developed and applied methods to transfer polygenic risk scores across ancestries, revealing both the promise and the current limitations of genetic risk prediction outside European populations.

Identifying associated regions, however, is only the first step. The vast majority of multiple sclerosis risk variants do not fall within protein-coding genes; instead, they cluster in regulatory regions of the genome—enhancers, promoters, and other noncoding elements that control when and where genes are switched on. To interpret these variants, the researchers assembled an extensive collection of multiomics datasets spanning the cell types most relevant to the disease. This included single-cell RNA sequencing to profile gene expression, single-cell assay for transposase-accessible chromatin sequencing (scATAC-seq) to map open, actively regulatory chromatin, and epigenomic annotations such as histone modification marks that flag active enhancers and promoters. The datasets covered immune cells critical to the periphery of the disease—such as CD4-positive and CD8-positive T cells, B cells, monocytes, and natural killer cells—as well as central nervous system resident cells, including microglia, astrocytes, oligodendrocytes, and their precursors.

By integrating GWAS summary statistics with these cellular maps through statistical frameworks such as stratified linkage disequilibrium score regression and transcriptome-wide and chromatin-interaction-based colocalization analyses, the team could ask a deceptively simple question with profound implications: in which cells, and at which anatomical and developmental stages, does the genetic risk of multiple sclerosis actually operate? The answers were striking. Genetic risk was strongly enriched in regulatory elements active in immune cell populations, particularly those involved in T cell activation and adaptive immune responses, consistent with the established immunopathology of the disease. But the analyses also revealed significant enrichment in resident central nervous system cells—most notably microglia, the brain’s innate immune macrophages—supporting an emerging model in which both peripheral immune cells and CNS-resident cells contribute to disease initiation and progression.

Perhaps the most innovative aspect of the study was its spatiocellular dimension. Rather than treating tissues as homogeneous mixtures, the researchers leveraged spatially resolved transcriptomic data to map genetic risk onto defined anatomical regions of the brain and spinal cord. This approach revealed that risk variants were not uniformly distributed across the nervous system: certain regulatory programs, active in specific regions and specific cell populations within those regions, showed disproportionate enrichment of heritability. The findings suggest that the spatial context of gene regulation—where in the central nervous system a risk variant’s target gene is active—helps determine how genetic susceptibility manifests as focal inflammatory lesions, the histological hallmark of multiple sclerosis. This spatial perspective, enabled by recent advances in spatial transcriptomics, opens a new dimension in the interpretation of complex disease genetics that conventional bulk and even single-cell analyses cannot capture.

The multiomics integration also enabled the researchers to prioritize causal genes at risk loci, a task that has historically been one of the hardest problems in post-GWAS biology. At many loci, the nearest gene to a risk variant is not the gene through which the variant acts. Using chromatin contacts, expression quantitative trait locus (eQTL) data, and colocalization of association signals with gene expression, the team linked noncoding risk variants to their distal target genes across cell types. Several prioritized genes converged on biologically coherent pathways, including antigen presentation, cytokine signaling, T cell receptor signaling, and interferon response—pathways that are already the targets of existing therapies and that point toward new therapeutic opportunities. Notably, the integration of cross-ancestry data sharpened many of these gene mappings, because fine-mapping resolution improved when linkage disequilibrium patterns from multiple populations were combined.

The clinical implications of the work are considerable. More precise fine-mapping of causal variants improves the foundation for polygenic risk scores, which could eventually aid in identifying individuals at elevated risk before symptom onset, particularly given that early treatment of multiple sclerosis is associated with substantially better outcomes. The study’s cross-ancestry framework also represents a corrective to a long-standing inequity in human genetics: individuals of non-European ancestry have been markedly underrepresented in GWAS, which has limited the accuracy of genetic risk prediction and the generalizability of biological conclusions worldwide. By demonstrating that multiancestry designs yield novel loci and finer resolution even for well-studied diseases, the research provides a template that other consortia studying complex diseases—from type 1 diabetes to rheumatoid arthritis to systemic lupus erythematosus—can follow.

The study also deepens understanding of the immunology of multiple sclerosis at a moment when therapeutics are rapidly evolving. Modern disease-modifying treatments, including anti-CD20 B cell depletion, S1P receptor modulators, and high-efficacy induction therapies, have transformed the disease course for many patients, but none reliably halt progression, and progression independent of relapse activity remains a major unmet need. The identification of genetic risk operating within microglia and other CNS-resident cells offers a mechanistic bridge between the peripheral immune processes targeted by current drugs and the compartmentalized central nervous system inflammation thought to drive progressive disease. Genes and regulatory programs prioritized through the spatiocellular analyses may point to targets capable of modulating the resident immune environment of the brain—therapeutic territory that has so far been difficult to reach.

As with any genetic study, important caveats remain. Fine-mapped variants are candidates, not proof; functional validation in experimental systems will be needed to confirm the causal mechanisms at each locus. The multiomics atlases, while extensive, still incompletely capture the full cellular diversity of human immune and nervous tissue, particularly in disease-relevant states such as activated microglia within lesions or tissue-resident lymphocyte populations. And even with multiancestry data, sample sizes for some ancestry groups remain modest relative to European cohorts, meaning that further global expansion of genetic studies will be needed to complete the picture. The authors and the broader field regard this work as a foundation rather than an endpoint: a demonstration that when genetics is combined with cellular, epigenomic, and spatial context across ancestrally diverse populations, the biology buried within genome-wide association signals becomes dramatically clearer.

Taken together, the study marks a turning point in multiple sclerosis genetics. It moves the field from lists of associated genomic regions toward a mechanistic, four-dimensional view of disease risk—one that incorporates cell type, gene regulatory circuitry, and anatomical location, and that embraces the full breadth of human genetic diversity. For a disease that has confounded researchers for more than a century, that perspective may prove to be the key to translating three decades of genetic discovery into therapies that work for every patient, in every population, at every stage of disease.