Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Towards a global view of multiple sclerosis genetics

Abstract

Multiple sclerosis (MS) is a neuroimmunological disorder of the CNS with a strong heritable component. The genetic architecture of MS susceptibility is well understood in populations of European ancestry. However, the extent to which this architecture explains MS susceptibility in populations of non-European ancestry remains unclear. In this Perspective article, we outline the scientific arguments for studying MS genetics in ancestrally diverse populations. We argue that this approach is likely to yield insights that could benefit individuals with MS from all ancestral groups. We explore the logistical and theoretical challenges that have held back this field to date and conclude that, despite these challenges, inclusion of participants of non-European ancestry in MS genetics studies will ultimately be of value to all patients with MS worldwide.

This is a preview of subscription content, access via your institution

Access options

Buy article

Get time limited or full article access on ReadCube.

$32.00

All prices are NET prices.

Fig. 1: Global variation in frequency of the HLA-DRB1*15:01 allele.
Fig. 2: Illustration of cross-ancestral fine mapping.

References

  1. Bentley, A. R., Callier, S. L. & Rotimi, C. N. Evaluating the promise of inclusion of African ancestry populations in genomics. NPJ Genom. Med. 5, 5 (2020).

    PubMed  PubMed Central  Google Scholar 

  2. Fatumo, S. et al. A roadmap to increase diversity in genomic studies. Nat. Med. 28, 243–250 (2022).

    CAS  PubMed  Google Scholar 

  3. Morales, J. et al. A standardized framework for representation of ancestry data in genomics studies, with application to the NHGRI-EBI GWAS Catalog. Genome Biol. 19, 21 (2018).

    PubMed  PubMed Central  Google Scholar 

  4. Landry, L. G., Ali, N., Williams, D. R., Rehm, H. L. & Bonham, V. L. Lack of diversity in genomic databases is a barrier to translating precision medicine research into practice. Health Aff. 37, 780–785 (2018).

    Google Scholar 

  5. Hindorff, L. A. et al. Prioritizing diversity in human genomics research. Nat. Rev. Genet. 19, 175–185 (2018).

    CAS  PubMed  Google Scholar 

  6. Ben-Eghan, C. et al. Don’t ignore genetic data from minority populations. Nature 585, 184–186 (2020).

    CAS  PubMed  Google Scholar 

  7. International Multiple Sclerosis Genetics Consortium (IMSGC) Analysis of immune-related loci identifies 48 new susceptibility variants for multiple sclerosis. Nat. Genet. 45, 1353–1360 (2013).

    Google Scholar 

  8. International Multiple Sclerosis Genetics Consortium. Multiple sclerosis genomic map implicates peripheral immune cells and microglia in susceptibility. Science 365, eaav7188 (2019).

    PubMed Central  Google Scholar 

  9. Isobe, N. et al. An ImmunoChip study of multiple sclerosis risk in African Americans. Brain 138, 1518–1530 (2015).

    PubMed  PubMed Central  Google Scholar 

  10. Isobe, N. et al. Genetic risk variants in African Americans with multiple sclerosis. Neurology 81, 219–227 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  11. Pandit, L. et al. Evaluation of the established non-MHC multiple sclerosis loci in an Indian population. Mult. Scler. 17, 139–143 (2011).

    CAS  PubMed  Google Scholar 

  12. Pandit, L. et al. HLA associations in South Asian multiple sclerosis. Mult. Scler. 22, 19–24 (2016).

    CAS  PubMed  Google Scholar 

  13. Oksenberg, J. R. et al. Mapping multiple sclerosis susceptibility to the HLA-DR locus in African Americans. Am. J. Hum. Genet. 74, 160–167 (2004).

    CAS  PubMed  Google Scholar 

  14. Reich, D. et al. A whole-genome admixture scan finds a candidate locus for multiple sclerosis susceptibility. Nat. Genet. 37, 1113–1118 (2005).

    CAS  PubMed  Google Scholar 

  15. Nakatsuka, N. et al. Two genetic variants explain the association of European ancestry with multiple sclerosis risk in African-Americans. Sci. Rep. 10, 16902 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  16. Sirugo, G., Williams, S. M. & Tishkoff, S. A. The missing diversity in human genetic studies. Cell 177, 1080 (2019).

    CAS  PubMed  Google Scholar 

  17. Duncan, L. et al. Analysis of polygenic risk score usage and performance in diverse human populations. Nat. Commun. 10, 3328 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  18. Peterson, R. E. et al. Genome-wide association studies in ancestrally diverse populations: opportunities, methods, pitfalls, and recommendations. Cell 179, 589–603 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  19. Martin, A. R. et al. Clinical use of current polygenic risk scores may exacerbate health disparities. Nat. Genet. 51, 584–591 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  20. Lam, M. et al. Comparative genetic architectures of schizophrenia in East Asian and European populations. Nat. Genet. 51, 1670–1678 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  21. Mahajan, A. et al. Trans-ethnic fine mapping highlights kidney-function genes linked to salt sensitivity. Am. J. Hum. Genet. 99, 636–646 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  22. Malik, R. et al. Multiancestry genome-wide association study of 520,000 subjects identifies 32 loci associated with stroke and stroke subtypes. Nat. Genet. 50, 524–537 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  23. Chen, M.-H. et al. Trans-ethnic and ancestry-specific blood-cell genetics in 746,667 individuals from 5 global populations. Cell 182, 1198–1213.e14 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  24. Graham, S. E. et al. The power of genetic diversity in genome-wide association studies of lipids. Nature 600, 675–679 (2021).

    CAS  PubMed  PubMed Central  Google Scholar 

  25. Laufer, V. A. et al. Genetic influences on susceptibility to rheumatoid arthritis in African-Americans. Hum. Mol. Genet. 28, 858–874 (2019).

    CAS  PubMed  Google Scholar 

  26. Robertson, C. C. et al. Fine-mapping, trans-ancestral and genomic analyses identify causal variants, cells, genes and drug targets for type 1 diabetes. Nat. Genet. 53, 962–971 (2021).

    CAS  PubMed  PubMed Central  Google Scholar 

  27. Onengut-Gumuscu, S. et al. Type 1 diabetes risk in African-ancestry participants and utility of an ancestry-specific genetic risk score. Diabetes Care 42, 406–415 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  28. Liu, J. Z. et al. Association analyses identify 38 susceptibility loci for inflammatory bowel disease and highlight shared genetic risk across populations. Nat. Genet. 47, 979–986 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  29. Somineni, H. K. et al. Whole-genome sequencing of African Americans implicates differential genetic architecture in inflammatory bowel disease. Am. J. Hum. Genet. 108, 431–445 (2021).

    CAS  PubMed  PubMed Central  Google Scholar 

  30. GBD 2016 Multiple Sclerosis Collaborators. Global, regional, and national burden of multiple sclerosis 1990-2016: a systematic analysis for the Global Burden of Disease Study 2016. Lancet Neurol. 18, 269–285 (2019).

    Google Scholar 

  31. Koch-Henriksen, N. & Sørensen, P. S. The changing demographic pattern of multiple sclerosis epidemiology. Lancet Neurol. 9, 520–532 (2010).

    PubMed  Google Scholar 

  32. Lee, J. D. et al. Incidence of multiple sclerosis and related disorders in Asian populations of British Columbia. Can. J. Neurol. Sci. 42, 235–241 (2015).

    PubMed  Google Scholar 

  33. Wallin, M. T. et al. The Gulf War era multiple sclerosis cohort: age and incidence rates by race, sex and service. Brain 135, 1778–1785 (2012).

    PubMed  Google Scholar 

  34. Langer-Gould, A., Brara, S. M., Beaber, B. E. & Zhang, J. L. Incidence of multiple sclerosis in multiple racial and ethnic groups. Neurology 80, 1734–1739 (2013).

    PubMed  Google Scholar 

  35. Dobson, R. et al. Ethnic and socioeconomic associations with multiple sclerosis risk. Ann. Neurol. 87, 599–608 (2020).

    PubMed  Google Scholar 

  36. Langer-Gould, A. M., Gonzales, E. G., Smith, J. B., Li, B. H. & Nelson, L. M. Racial and ethnic disparities in multiple sclerosis prevalence. Neurology 98, e1818–e1827 (2022).

    CAS  PubMed  PubMed Central  Google Scholar 

  37. Munk Nielsen, N. et al. Multiple sclerosis among first- and second-generation immigrants in Denmark: a population-based cohort study. Brain 142, 1587–1597 (2019).

    PubMed  Google Scholar 

  38. Ahlgren, C., Odén, A. & Lycke, J. A nationwide survey of the prevalence of multiple sclerosis in immigrant populations of Sweden. Mult. Scler. 18, 1099–1107 (2012).

    PubMed  Google Scholar 

  39. Sawcer, S. et al. A high-density screen for linkage in multiple sclerosis. Am. J. Hum. Genet. 77, 454–467 (2005).

    PubMed  Google Scholar 

  40. Jersild, C., Svejgaard, A. & Fog, T. HL-A antigens and multiple sclerosis. Lancet 1, 1240–1241 (1972).

    CAS  PubMed  Google Scholar 

  41. Moutsianas, L. et al. Class II HLA interactions modulate genetic risk for multiple sclerosis. Nat. Genet. 47, 1107–1113 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  42. Jokubaitis, V. G. et al. Not all roads lead to the immune system: the genetic basis of multiple sclerosis severity implicates central nervous system and mitochondrial involvement. Preprint at medRxiv https://doi.org/10.1101/2022.02.04.22270362 (2022).

    Article  Google Scholar 

  43. Vandebergh, M. et al. Genetic variation in WNT9B increases relapse hazard in multiple sclerosis. Ann. Neurol. 89, 884–894 (2021).

    CAS  PubMed  PubMed Central  Google Scholar 

  44. Dendrou, C. A., Petersen, J., Rossjohn, J. & Fugger, L. HLA variation and disease. Nat. Rev. Immunol. 18, 325–339 (2018).

    CAS  PubMed  Google Scholar 

  45. Hollenbach, J. A. & Oksenberg, J. R. The immunogenetics of multiple sclerosis: a comprehensive review. J. Autoimmun. 64, 13–25 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  46. Yoshimura, S. et al. Genetic and infectious profiles of Japanese multiple sclerosis patients. PLoS ONE 7, e48592 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  47. Saruhan-Direskeneli, G. et al. HLA-DR and -DQ associations with multiple sclerosis in Turkey. Hum. Immunol. 55, 59–65 (1997).

    CAS  PubMed  Google Scholar 

  48. Alvarado-de la Barrera, C. et al. HLA class II genotypes in Mexican Mestizos with familial and nonfamilial multiple sclerosis. Neurology 55, 1897–1900 (2000).

    CAS  PubMed  Google Scholar 

  49. Brassat, D. et al. The HLA locus and multiple sclerosis in Sicily. Neurology 64, 361–363 (2005).

    CAS  PubMed  Google Scholar 

  50. Nakamura, Y. et al. Latitude and HLA-DRB1*04:05 independently influence disease severity in Japanese multiple sclerosis: a cross-sectional study. J. Neuroinflamm. 13, 239 (2016).

    Google Scholar 

  51. Watanabe, M. et al. HLA genotype-clinical phenotype correlations in multiple sclerosis and neuromyelitis optica spectrum disorders based on Japan MS/NMOSD Biobank data. Sci. Rep. 11, 607 (2021).

    CAS  PubMed  PubMed Central  Google Scholar 

  52. Amirzargar, A. et al. HLA class II (DRB1, DQA1 and DQB1) associated genetic susceptibility in Iranian multiple sclerosis (MS) patients. Eur. J. Immunogenet. 25, 297–301 (1998).

    CAS  PubMed  Google Scholar 

  53. Brum, D. G., Barreira, A. A., Louzada-Junior, P., Mendes-Junior, C. T. & Donadi, E. A. Association of the HLA-DRB1*15 allele group and the DRB1*1501 and DRB1*1503 alleles with multiple sclerosis in White and Mulatto samples from Brazil. J. Neuroimmunol. 189, 118–124 (2007).

    CAS  PubMed  Google Scholar 

  54. Quelvennec, E. et al. Genetic and functional studies in multiple sclerosis patients from Martinique attest for a specific and direct role of the HLA-DR locus in the syndrome. Tissue Antigens 61, 166–171 (2003).

    CAS  PubMed  Google Scholar 

  55. Khankhanian, P. et al. Genetic contribution to multiple sclerosis risk among Ashkenazi Jews. BMC Med. Genet. 16, 55 (2015).

    PubMed  PubMed Central  Google Scholar 

  56. Kwon, O. J. et al. HLA class II susceptibility to multiple sclerosis among Ashkenazi and non-Ashkenazi Jews. Arch. Neurol. 56, 555–560 (1999).

    CAS  PubMed  Google Scholar 

  57. Marrosu, M. G. et al. Dissection of the HLA association with multiple sclerosis in the founder isolated population of Sardinia. Hum. Mol. Genet. 10, 2907–2916 (2001).

    CAS  PubMed  Google Scholar 

  58. Goodin, D. S., Oksenberg, J. R., Douillard, V., Gourraud, P.-A. & Vince, N. Genetic susceptibility to multiple sclerosis in African Americans. PLoS ONE 16, e0254945 (2021).

    CAS  PubMed  PubMed Central  Google Scholar 

  59. Chi, C. et al. Admixture mapping reveals evidence of differential multiple sclerosis risk by genetic ancestry. PLoS Genet. 15, e1007808 (2019).

    PubMed  PubMed Central  Google Scholar 

  60. Rivera, V. M. Multiple sclerosis in Latin Americans: genetic aspects. Curr. Neurol. Neurosci. Rep. 17, 57 (2017).

    PubMed  Google Scholar 

  61. Vinoy, N., Sheeja, N., Kumar, S. & Biswas, L. Class II HLA (DRB1, & DQB1) alleles and IL7R (rs6897932) variants and the risk for multiple sclerosis in Kerala, India. Mult. Scler. Relat. Disord. 50, 102848 (2021).

    CAS  PubMed  Google Scholar 

  62. International HapMap Consortium. A second generation human haplotype map of over 3.1 million SNPs. Nature 449, 851–861 (2007).

    Google Scholar 

  63. Matsuki, K., Carl Grumet, F., Lin, X., Gelb, M. & Gueilleminault, C. DQ (rather than DR) gene marks susceptibility to narcolepsy. Lancet 339, 1052 (1992).

    CAS  PubMed  Google Scholar 

  64. Okada, Y. et al. Contribution of a non-classical HLA gene, HLA-DOA, to the risk of rheumatoid arthritis. Am. J. Hum. Genet. 99, 366–374 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  65. Naito, T. et al. A deep learning method for HLA imputation and trans-ethnic MHC fine-mapping of type 1 diabetes. Nat. Commun. 12, 1639 (2021).

    CAS  PubMed  PubMed Central  Google Scholar 

  66. Patsopoulos, N. A. et al. Fine-mapping the genetic association of the major histocompatibility complex in multiple sclerosis: HLA and non-HLA effects. PLoS Genet. 9, e1003926 (2013).

    PubMed  PubMed Central  Google Scholar 

  67. Luo, Y. et al. A high-resolution HLA reference panel capturing global population diversity enables multi-ancestry fine-mapping in HIV host response. Nat. Genet. 53, 1504–1516 (2021).

    CAS  PubMed  PubMed Central  Google Scholar 

  68. Beecham, A. H. et al. The genetic diversity of multiple sclerosis risk among Hispanic and African American populations living in the United States. Mult. Scler. 26, 1329–1339 (2019).

    PubMed  PubMed Central  Google Scholar 

  69. Johnson, B. A. et al. Multiple sclerosis susceptibility alleles in African Americans. Genes Immun. 11, 343–350 (2010).

    CAS  PubMed  Google Scholar 

  70. Hilven, K. & Goris, A. Genetic burden mirrors epidemiology of multiple sclerosis. Mult. Scler. 21, 1353–1354 (2015).

    CAS  PubMed  Google Scholar 

  71. Hadjigeorgiou, G. M. et al. Replication study of GWAS risk loci in Greek multiple sclerosis patients. Neurol. Sci. 40, 253–260 (2019).

    PubMed  Google Scholar 

  72. Pandit, L. et al. European multiple sclerosis risk variants in the south Asian population. Mult. Scler. 22, 1536–1540 (2016).

    CAS  PubMed  Google Scholar 

  73. Kira, J., Matsushita, T., Sato, S. & Yamamoto, K. A genome-wide association study (GWAS) in the Japanese population reveals novel genetic risk factors for multiple sclerosis and neuromyelitis optica. J. Neurol. Sci. 357, e308 (2015).

    Google Scholar 

  74. Weissbrod, O. et al. Leveraging fine-mapping and multipopulation training data to improve cross-population polygenic risk scores. Nat. Genet. 54, 450–458 (2022).

    CAS  PubMed  Google Scholar 

  75. Cortes, A. & Brown, M. A. Promise and pitfalls of the immunochip. Arthritis Res. Ther. 13, 101 (2011).

    PubMed  PubMed Central  Google Scholar 

  76. Beecham, A. H. & McCauley, J. L. Fine-mapping array design for multi-ethnic studies of multiple sclerosis. Genes 10, 903 (2019).

    CAS  PubMed Central  Google Scholar 

  77. Jokubaitis, V. G., Zhou, Y., Butzkueven, H. & Taylor, B. V. Genotype and phenotype in multiple sclerosis–potential for disease course prediction? Curr. Treat. Options Neurol. 20, 18 (2018).

    PubMed  Google Scholar 

  78. International Multiple Sclerosis Genetics Consortium & The Wellcome Trust Case Control Consortium 2. Genetic risk and a primary role for cell-mediated immune mechanisms in multiple sclerosis. Nature 476, 214–219 (2011).

    Google Scholar 

  79. Zhou, Y. et al. Genetic variation in the gene LRP2 increases relapse risk in multiple sclerosis. J. Neurol. Neurosurg. Psychiatry 88, 864–868 (2017).

    PubMed  Google Scholar 

  80. Barnett, I. J., Lee, S. & Lin, X. Detecting rare variant effects using extreme phenotype sampling in sequencing association studies. Genet. Epidemiol. 37, 142–151 (2013).

    PubMed  Google Scholar 

  81. Padmanabhan, S. et al. Genome-wide association study of blood pressure extremes identifies variant near UMOD associated with hypertension. PLoS Genet. 6, e1001177 (2010).

    PubMed  PubMed Central  Google Scholar 

  82. Berndt, S. I. et al. Genome-wide meta-analysis identifies 11 new loci for anthropometric traits and provides insights into genetic architecture. Nat. Genet. 45, 501–512 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  83. Emond, M. J. et al. Exome sequencing of extreme phenotypes identifies DCTN4 as a modifier of chronic Pseudomonas aeruginosa infection in cystic fibrosis. Nat. Genet. 44, 886–889 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  84. Boora, G. K. et al. Testing of candidate single nucleotide variants associated with paclitaxel neuropathy in the trial NCCTG N08C1 (Alliance). Cancer Med. 5, 631–639 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  85. Crouch, D. J. M. et al. Genetics of the human face: identification of large-effect single gene variants. Proc. Natl Acad. Sci. USA 115, E676–E685 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  86. Weinstock-Guttman, B. et al. Multiple sclerosis characteristics in African American patients in the New York State Multiple Sclerosis Consortium. Mult. Scler. 9, 293–298 (2003).

    CAS  PubMed  Google Scholar 

  87. Ventura, R. E., Antezana, A. O., Bacon, T. & Kister, I. Hispanic Americans and African Americans with multiple sclerosis have more severe disease course than Caucasian Americans. Mult. Scler. 23, 1554–1557 (2017).

    PubMed  Google Scholar 

  88. Gray-Roncal, K. et al. Association of disease severity and socioeconomic status in Black and White Americans with multiple sclerosis. Neurology https://doi.org/10.1212/WNL.0000000000012362 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  89. Hadjixenofontos, A. et al. Clinical expression of multiple sclerosis in Hispanic whites of primarily Caribbean ancestry. Neuroepidemiology 44, 262–268 (2015).

    PubMed  Google Scholar 

  90. Amezcua, L., Lund, B. T., Weiner, L. P. & Islam, T. Multiple sclerosis in Hispanics: a study of clinical disease expression. Mult. Scler. 17, 1010–1016 (2011).

    CAS  PubMed  Google Scholar 

  91. Amezcua, L. et al. Native ancestry is associated with optic neuritis and age of onset in Hispanics with multiple sclerosis. Ann. Clin. Transl. Neurol. 5, 1362–1371 (2018).

    PubMed  PubMed Central  Google Scholar 

  92. Caldito, N. G. et al. Brain and retinal atrophy in African-Americans versus Caucasian-Americans with multiple sclerosis: a longitudinal study. Brain 141, 3115–3129 (2018).

    PubMed  PubMed Central  Google Scholar 

  93. Kimbrough, D. J. et al. Retinal damage and vision loss in African American multiple sclerosis patients. Ann. Neurol. 77, 228–236 (2015).

    PubMed  PubMed Central  Google Scholar 

  94. Howard, J. et al. MRI correlates of disability in African-Americans with multiple sclerosis. PLoS ONE 7, e43061 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  95. Kister, I. et al. Rapid disease course in African Americans with multiple sclerosis. Neurology 75, 217–223 (2010).

    CAS  PubMed  Google Scholar 

  96. Khan, O. et al. Multiple sclerosis in US minority populations: clinical practice insights. Neurol. Clin. Pract. 5, 132–142 (2015).

    PubMed  PubMed Central  Google Scholar 

  97. Cree, B. A. C. et al. Clinical characteristics of African Americans vs Caucasian Americans with multiple sclerosis. Neurology 63, 2039–2045 (2004).

    CAS  PubMed  Google Scholar 

  98. Naismith, R. T., Trinkaus, K. & Cross, A. H. Phenotype and prognosis in African-Americans with multiple sclerosis: a retrospective chart review. Mult. Scler. 12, 775–781 (2006).

    CAS  PubMed  Google Scholar 

  99. Kister, I., Bacon, T. & Cutter, G. R. How multiple sclerosis symptoms vary by age, sex, and race/ethnicity. Neurol. Clin. Pract. 11, 335–341 (2021).

    PubMed  PubMed Central  Google Scholar 

  100. Jamal, I. et al. Multiple sclerosis in Kenya: demographic and clinical characteristics of a registry cohort. Mult. Scler. J. Exp. Transl. Clin. 7, 20552173211022784 (2021).

    Google Scholar 

  101. Sanna, S. et al. Variants within the immunoregulatory CBLB gene are associated with multiple sclerosis. Nat. Genet. 42, 495–497 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  102. Orrù, V. et al. Genetic variants regulating immune cell levels in health and disease. Cell 155, 242–256 (2013).

    PubMed  PubMed Central  Google Scholar 

  103. Sidore, C. et al. Genome sequencing elucidates Sardinian genetic architecture and augments association analyses for lipid and blood inflammatory markers. Nat. Genet. 47, 1272–1281 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  104. Steri, M. et al. Overexpression of the cytokine BAFF and autoimmunity risk. N. Engl. J. Med. 376, 1615–1626 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  105. Shriner, D. Overview of admixture mapping. Curr. Protoc. Hum. Genet. https://doi.org/10.1002/0471142905.hg0123s76 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  106. Romanelli, R. J. et al. Multiple sclerosis in a multi-ethnic population from Northern California: a retrospective analysis, 2010–2016. BMC Neurol. 20, 163 (2020).

    PubMed  PubMed Central  Google Scholar 

  107. Caliskan, M., Brown, C. D. & Maranville, J. C. A catalog of GWAS fine-mapping efforts in autoimmune disease. Am. J. Hum. Genet. 108, 549–563 (2021).

    CAS  PubMed  PubMed Central  Google Scholar 

  108. Wang, Y. et al. Theoretical and empirical quantification of the accuracy of polygenic scores in ancestry divergent populations. Nat. Commun. 11, 3865 (2020).

    PubMed  PubMed Central  Google Scholar 

  109. Li, Y. R. & Keating, B. J. Trans-ethnic genome-wide association studies: advantages and challenges of mapping in diverse populations. Genome Med. 6, 91 (2014).

    PubMed  PubMed Central  Google Scholar 

  110. International Multiple Sclerosis Genetics Consortium. A systems biology approach uncovers cell-specific gene regulatory effects of genetic associations in multiple sclerosis. Nat. Commun. 10, 2236 (2019).

    Google Scholar 

  111. Jacobs, B. M. et al. Gene-environment interactions in multiple sclerosis: a UK Biobank Study. Neurol. Neuroimmunol. Neuroinflamm. https://doi.org/10.1212/NXI.0000000000001007 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  112. Privé, F. et al. Portability of 245 polygenic scores when derived from the UK Biobank and applied to 9 ancestry groups from the same cohort. Am. J. Hum. Genet. 109, 373 (2022).

    PubMed  PubMed Central  Google Scholar 

  113. Márquez-Luna, C., Loh, P.-R., South Asian Type 2 Diabetes (SAT2D) Consortium, SIGMA Type 2 Diabetes Consortium & Price, A. R. Multiethnic polygenic risk scores improve risk prediction in diverse populations. Genet. Epidemiol. 41, 811–823 (2017).

    PubMed  PubMed Central  Google Scholar 

  114. Amariuta, T. et al. Improving the trans-ancestry portability of polygenic risk scores by prioritizing variants in predicted cell-type-specific regulatory elements. Nat. Genet. 52, 1346–1354 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  115. Martin, A. R. et al. Human demographic history impacts genetic risk prediction across diverse populations. Am. J. Hum. Genet. 100, 635–649 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  116. Harroud, A. et al. Childhood obesity and multiple sclerosis: a Mendelian randomization study. Mult. Scler. 27, 2150–2158 (2021).

    CAS  PubMed  Google Scholar 

  117. Jacobs, B. M., Noyce, A. J., Giovannoni, G. & Dobson, R. BMI and low vitamin D are causal factors for multiple sclerosis: a Mendelian randomization study. Neurol. Neuroimmunol. Neuroinflamm 7, e662 (2020).

    PubMed  PubMed Central  Google Scholar 

  118. Vandebergh, M. & Goris, A. Smoking and multiple sclerosis risk: a Mendelian randomization study. J. Neurol. 267, 3083–3091 (2020).

    PubMed  PubMed Central  Google Scholar 

  119. Mitchell, R. E. et al. Little evidence for an effect of smoking on multiple sclerosis risk: A Mendelian randomization study. PLOS Biol. 18, e3000973 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  120. Harroud, A. et al. Effect of age at puberty on risk of multiple sclerosis: a Mendelian randomization study. Neurology 92, e1803–e1810 (2019).

    PubMed  PubMed Central  Google Scholar 

  121. Fatumo, S. et al. Metabolic traits and stroke risk in individuals of African ancestry: Mendelian randomization analysis. Stroke 52, 2680–2684 (2021).

    CAS  PubMed  PubMed Central  Google Scholar 

  122. Finer, S. et al. Cohort profile: East London Genes & Health (ELGH), a community-based population genomics and health study in British Bangladeshi and British Pakistani people. Int. J. Epidemiol. 49, 20–21i (2020).

    PubMed  Google Scholar 

  123. All of Us Research Program Investigators. The “All of Us” Research Program. N. Engl. J. Med. 381, 668–676 (2019).

    Google Scholar 

  124. Alexander, D. H. & Lange, K. Enhancements to the ADMIXTURE algorithm for individual ancestry estimation. BMC Bioinformatics 12, 246 (2011).

    PubMed  PubMed Central  Google Scholar 

  125. Maples, B. K., Gravel, S., Kenny, E. E. & Bustamante, C. D. RFMix: a discriminative modeling approach for rapid and robust local-ancestry inference. Am. J. Hum. Genet. 93, 278–288 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  126. Atkinson, E. G. et al. Tractor uses local ancestry to enable the inclusion of admixed individuals in GWAS and to boost power. Nat. Genet. 53, 195–204 (2021).

    CAS  PubMed  PubMed Central  Google Scholar 

  127. Brown, B. C., Ye, C. J., Price, A. L., Zaitlen, N. & Asian Genetic Epidemiology Network Type 2 Diabetes Consortium. Transethnic genetic-correlation estimates from summary statistics. Am. J. Hum. Genet. 99, 76–88 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  128. Ruan, Y. et al. Improving polygenic prediction in ancestrally diverse populations. Nat. Genet. 54, 573–580 (2022).

    CAS  PubMed  Google Scholar 

  129. Huang, Q. Q. et al. Transferability of genetic loci and polygenic scores for cardiometabolic traits in British Pakistani and Bangladeshi individuals. Nat. Commun. 13, 4664 (2022).

    CAS  PubMed  PubMed Central  Google Scholar 

  130. Taliun, D. et al. Sequencing of 53,831 diverse genomes from the NHLBI TOPMed program. Nature 590, 290–299 (2021).

    CAS  PubMed  PubMed Central  Google Scholar 

  131. Kraft, S. A. et al. Beyond consent: building trusting relationships with diverse populations in precision medicine research. Am. J. Bioeth. 18, 3–20 (2018).

    PubMed  PubMed Central  Google Scholar 

  132. Nuriddin, A., Mooney, G. & White, A. I. R. Reckoning with histories of medical racism and violence in the USA. Lancet 396, 949–951 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  133. Schaid, D. J., Chen, W. & Larson, N. B. From genome-wide associations to candidate causal variants by statistical fine-mapping. Nat. Rev. Genet. 19, 491–504 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

The authors thank S. Sawcer, University of Cambridge, UK, for helpful comments on an early draft of the manuscript. B.M.J. is supported by an MRC Clinical Research Training Fellowship (grant reference MR/V028766/1).

Author information

Authors and Affiliations

Authors

Contributions

B.M.J. researched data for the article. B.M.J., G.G., A.J.N., H.R.M. and R.D. wrote the article. All authors made substantial contributions to discussion of the content and reviewed and edited the manuscript before submission.

Corresponding author

Correspondence to Benjamin Meir Jacobs.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature Reviews Neurology thanks L. Amezcua; N. Isobe; J. McCauley, who co-reviewed with A. Beecham; M. Williams and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Jacobs, B.M., Peter, M., Giovannoni, G. et al. Towards a global view of multiple sclerosis genetics. Nat Rev Neurol 18, 613–623 (2022). https://doi.org/10.1038/s41582-022-00704-y

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41582-022-00704-y

This article is cited by

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing