Mapping Multiple Sclerosis Genetics Across Ancestries: Insights from South Asian and African Populations
Multiple Sclerosis (MS) is a complex immune-mediated disease of the central nervous system in which genetic susceptibility interacts with environmental and immunological factors. For decades, genetic studies of MS have been dominated by cohorts of European ancestry, leading to the identification of more than 200 risk loci, including strong associations within the Major Histocompatibility Complex (MHC). However, the underrepresentation of non-European populations has limited the generalisability of genetic findings, reduced the accuracy of genetic risk prediction across populations, and constrained opportunities for multi-ancestry fine mapping. The article addresses this imbalance by examining the genetic determinants of MS susceptibility in individuals of South Asian and African ancestry living in the United Kingdom.
The Rationale for Multi-Ancestry MS Genetics
The central scientific motivation of the study is that genetic architecture can be shared across populations while still differing in allele frequencies, linkage disequilibrium patterns, and population-level impact. In MS, European-ancestry genome-wide association studies have shown that susceptibility is highly polygenic, with the MHC region contributing a particularly important component of risk. Yet variants that are common or informative in European populations may be rare, differently tagged, or less predictive in other ancestral groups. By studying diverse populations, researchers can test whether known susceptibility loci operate consistently across ancestries, identify ancestry-enriched risk alleles, and improve the biological resolution of disease-associated regions.
Study Design and Cohort Construction
The study used the ADAMS project, a United Kingdom-based initiative designed to recruit and genetically characterise people with MS from diverse ancestral backgrounds. Participants were recruited through clinical sites, an online platform, primary care, and the United Kingdom MS Register. Phenotypic data were obtained through structured questionnaires, including demographic information, MS subtype, age at diagnosis, treatment history, family history, migration history, adolescent body mass index, smoking history, glandular fever history, and disability-related measures. DNA was collected from saliva samples and genotyped using a commercial array, after which the investigators combined the case data with ancestrally similar controls from the UK Biobank.
Genetic Ancestry Inference and Analytical Strategy
A major methodological strength of the study was its careful approach to genetic ancestry inference and quality control. The authors used reference datasets from the Human Genome Diversity Project and the 1000 Genomes Project to classify participants into broad genetic ancestry groups. After excluding individuals with ambiguous ancestry and applying stringent within-ancestry quality control, the investigators analysed two case-control datasets: 175 MS cases and 6,744 controls of inferred South Asian ancestry, and 113 MS cases and 5,177 controls of inferred African ancestry. Genome-wide association analyses were performed within each ancestry group using models that accounted for sex and population structure, thereby reducing the risk of confounding by ancestry-related genetic variation.
MHC Associations Across South Asian and African Ancestries
The most prominent findings emerged from the MHC region on chromosome 6, which is already recognised as the strongest genetic susceptibility locus for MS. In the South Asian ancestry cohort, the leading signal was located near HLA-DRB1, with an odds ratio of 1.84, while in the African ancestry cohort the leading signal was near HLA-A, with an odds ratio of 2.24. Although these associations did not reach conventional genome-wide significance, they were consistent with the established centrality of antigen presentation pathways in MS susceptibility. The study also imputed classical HLA alleles and found several suggestive associations, including HLA-DRB1*15:01 in both ancestry groups, as well as additional alleles such as HLA-DPB1*10:01 and HLA-A*26:01 in South Asian ancestry and HLA-A*66:01 in African ancestry.
Shared Genetic Architecture and Limits of European-Derived Risk Scores
The investigators compared their findings with previously published European-ancestry MS GWAS results to evaluate cross-ancestry concordance. European-derived MS susceptibility alleles were generally over-represented among MS cases in both South Asian and African ancestry cohorts, with stronger evidence of concordance in the South Asian cohort. This supports the hypothesis that many MS risk mechanisms are shared across ancestral backgrounds. However, European-derived polygenic risk scores performed less well in these non-European cohorts than in European ancestry cohorts. The best-performing score explained approximately 1.6% of liability in the South Asian cohort and 0.5% in the African cohort, underscoring both the partial transferability of MS genetic risk models and the need for ancestry-inclusive discovery datasets.
Implications, Limitations, and Future Directions
This study represents an important step toward a more equitable and biologically complete understanding of MS genetics. Its findings reinforce the universal importance of the MHC region, support the existence of shared disease mechanisms across ancestries, and highlight the potential for ancestry-enriched alleles to refine causal inference. At the same time, the authors appropriately emphasise key limitations, including modest sample size, reliance on imputed HLA alleles rather than direct sequencing, possible batch effects from combining case and control cohorts, and limited power to detect novel genome-wide significant associations. The broader implication is clear: large-scale, international, multi-ancestry MS genetic studies will be essential for improving fine mapping, identifying novel drug targets, and developing genetic risk prediction tools that perform reliably across populations.
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:
Clarelli, F., Sorosina, M., Giordano, A., Mascia, E., Visentin, G., Missaglia, M., ... & Esposito, F. (2025). Contribution of Polygenic Scores to Progression Independent of Relapse Activity in Multiple Sclerosis. European journal of neurology, 32(7), e70264.
