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.

Central to this study is the demonstration that genetic ancestry profoundly impacts how patients respond to therapies, particularly glucagon-like peptide-1 receptor agonists (GLP-1 RAs), commonly prescribed for weight loss and type 2 diabetes. The researchers found that therapeutic efficacy of GLP-1 drugs, such as semaglutide, varies significantly across different ancestral populations, and critically, this variability correlates with individuals’ genetic risk scores for type 2 diabetes. Such findings underscore the limitations of one-size-fits-all treatment approaches and herald a new era where genetic insights inform tailored medical interventions.

Utilizing integrative proteogenomic analyses, the team pinpointed a key genetic association between response to semaglutide and the gene PTPRU. This gene had not previously been linked to GLP-1 drug response, offering compelling evidence for its role in modulating treatment outcomes. Proteomics data from patients undergoing GLP-1 therapy further reinforced these findings, providing a molecular bridge between genotypic variation and phenotypic drug responsiveness. This discovery paves the way for future mechanistic studies and the potential development of predictive biomarkers to optimize obesity and diabetes therapies.

The ATLAS Biobank uniquely encompasses an expansive representation of ancestries, reflecting Los Angeles’ unparalleled ethnic diversity. Participants hail from five continental ancestries and encompass thirty-six fine-scale ancestry groups, including communities historically underrepresented in genetic research such as Armenian, Ashkenazi Jews, Iranian Jewish, Filipino, and Mexican American populations. This breadth allows for the disentanglement of genetic influences on health outcomes without confounding by healthcare system disparities, a common challenge when comparing data across institutions.

Historically, the majority of genomic studies have disproportionately sampled populations of European descent, limiting the applicability of findings to the global population and exacerbating health disparities. The UCLA ATLAS initiative confronts this bias head-on by drawing from one of the world’s most ancestrally diverse metropolitan areas—Los Angeles County—which boasts over 9.6 million residents. By integrating diverse genetic data with longitudinal clinical records within a single health system, this study establishes a paradigm for equitable precision medicine research.

Beyond common genetic variants, the study pioneers examination of rare variants within specific ancestry groups, unveiling hitherto unknown genetic correlations with disease phenotypes. For instance, the gene ANKZF1 was linked to peripheral vascular disease among African ancestry individuals, while EPG5 was associated with lipid metabolism traits such as HDL cholesterol and triglyceride levels in Ashkenazi Jewish participants. These discoveries highlight the importance of including rare variant analyses in multi-ancestry cohorts to illuminate genetic contributions to complex diseases.

The investigation also delineated ancestry-specific susceptibilities to adverse drug reactions. Among Mexicans and South Americans, increased vulnerability to negative hormonal therapy effects was observed, reinforcing the need for ancestry-informed pharmacovigilance. This awareness is critical for improving drug safety profiles and optimizing treatment plans for diverse populations, thereby enhancing patient outcomes and reducing health inequities.

A further significant dimension of the research involves polygenic risk scores (PRS), composite metrics summarizing genetic predispositions to diseases based on numerous variants spread across the genome. Within the ATLAS cohort, PRS demonstrated promising predictive power for conditions like type 1 diabetes, with a substantial proportion of patients exhibiting elevated scores matching their clinical diagnoses. Though clinical translation remains in early stages, these findings position PRS as a valuable tool for stratifying patient risk and guiding preventive strategies.

The researchers’ focus on GLP-1 receptor agonists as a case study showcases how genetic diversity can influence response to commonly prescribed medications. GLP-1 drugs, including branded agents such as Ozempic and Wegovy, have revolutionized treatment for obesity and diabetes but exhibit variable efficacy among individuals. Identifying genetic markers like those in PTPRU provides a molecular rationale for this heterogeneity and suggests pathways to develop predictive algorithms to personalize therapy.

Importantly, the UCLA Health system’s comprehensive real-world data environment—linking genetics with electronic health records—affords robust insights into disease pathogenesis and therapeutic outcomes within a clinical context. This approach contrasts with isolated laboratory investigations, elevating the translational potential of discoveries. As Dr. Daniel Geschwind, senior associate dean of Precision Health at UCLA, notes, ATLAS’s integration of broad and fine-scale ancestries illuminates genetic factors overlooked in earlier studies focused on broad ancestral categories alone.

Already, the ATLAS Biobank supports a public web portal presenting thousands of heritable genetic associations across diverse populations, enabling researchers worldwide to access and build upon these unprecedented data. With over 259,000 participants consented and 157,000 biospecimens collected since its launch in 2016, this initiative embodies a scalable model for genomic medicine research embedded within large health systems, fostering health equity by design.

The implications of these findings extend far beyond the academic sphere. They propel precision medicine closer to practical application, where individual genomic profiles guide risk assessment, diagnosis, and personalized treatments. Furthermore, this study is a call to action emphasizing the necessity of inclusive genetic research that respects and reflects population diversity to fulfill the promise of equitable, effective healthcare for all.

In conclusion, the UCLA Health-led study published in Cell underscores the transformative impact of integrating genetic diversity, clinical data, and molecular biology within a single health ecosystem. It highlights novel genetic determinants influencing disease risk and drug response, particularly in relation to type 2 diabetes and weight loss medications. By bridging gaps in ancestry representation and leveraging comprehensive real-world data, the work sets a new standard for precision health discovery and clinical translation, demonstrating that personalized medicine is not just a possibility for some but an achievable goal for the global population.

Subject of Research: Human tissue samples
Article Title: Advancing Precision Health Discovery in a Genetically Diverse Health System
News Publication Date: 27-Mar-2026
Web References: [UCLA ATLAS Community Health Initiative Biobank Web Portal] (link not provided in source)
References: DOI: 10.1016/j.cell.2026.03.007
Keywords: precision medicine, genetic diversity, GLP-1 receptor agonists, type 2 diabetes, polygenic risk scores, ancestry, genetic associations, semaglutide, pharmacogenomics, health disparities, rare genetic variants, proteomics