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A whole-genome admixture scan finds a candidate locus for multiple sclerosis susceptibility

Nature Genetics volume 37, pages 1113–1118 (2005)Cite this article

Abstract

Multiple sclerosis is a common disease with proven heritability, but, despite large-scale attempts, no underlying risk genes have been identified. Traditional linkage scans have so far identified only one risk haplotype for multiple sclerosis (at HLA on chromosome 6), which explains only a fraction of the increased risk to siblings. Association scans such as admixture mapping have much more power, in principle, to find the weak factors that must explain most of the disease risk. We describe here the first high-powered admixture scan, focusing on 605 African American cases and 1,043 African American controls, and report a locus on chromosome 1 that is significantly associated with multiple sclerosis.

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Figure 1: Results of the genome-wide admixture scan (run 3 in Table 1).
Figure 2: The strongest peak of association spans the centromere of chromosome 1.
Figure 3: The 95% credible intervals for increased risk due to European ancestry at the chromosome 1 locus, for all African Americans (n = 605), all Afro-Caribbeans (n = 143) and both cohorts together (n = 748).

References

  1. Rife, D.C. Populations of hybrid origin as source material for the detection of linkage. Am. J. Hum. Genet. 6, 26–33 (1954).

    CAS  PubMed  PubMed Central  Google Scholar 

  2. Chakraborty, R. & Weiss, K.M. Admixture as a tool for finding linked genes and detecting that difference from allelic association between loci. Proc. Natl. Acad. Sci. USA 85, 9119–9123 (1988).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Stephens, J.C., Briscoe, D. & O'Brien, S.J. Mapping by admixture linkage disequilibrium in human populations: Limits and guidelines. Am. J. Hum. Genet. 55, 809–824 (1994).

    CAS  PubMed  PubMed Central  Google Scholar 

  4. Briscoe, D., Stephens, J.C. & O'Brien, S.J. Linkage disequilibrium in admixed populations: Applications in gene mapping. J. Hered. 85, 59–63 (1994).

    CAS  PubMed  Google Scholar 

  5. McKeigue, P.M. Mapping genes underlying ethnic differences in disease risk by linkage disequilibrium in recently admixed populations. Am. J. Hum. Genet. 60, 188–196 (1997).

    CAS  PubMed  PubMed Central  Google Scholar 

  6. McKeigue, P.M. Mapping genes that underlie ethnic differences in disease risk: methods for detecting linkage in populations, by conditioning on parental admixture. Am. J. Hum. Genet. 63, 241–251 (1998).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Smith, M.W. et al. A high-density admixture map for disease gene discovery in African Americans. Am. J. Hum. Genet. 74, 1001–1013 (2004).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Patterson, N. et al. Methods for high-density admixture mapping of disease genes. Am. J. Hum. Genet. 74, 979–1000 (2004).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Hoggart, C.J., Shriver, M.D., Kittles, R.A., Clayton, D.G. & McKeigue, P.M. Design and analysis of admixture mapping studies. Am. J. Hum. Genet. 74, 965–978 (2004).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Montana, G. & Pritchard, J.K. Statistical tests for admixture mapping with case-control and cases-only data. Am. J. Hum. Genet. 75, 771–789 (2004).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Zhu, X. et al. Admixture mapping for hypertension loci with genome-scan markers. Nat. Genet. 37, 177–181 (2005).

    Article  CAS  PubMed  Google Scholar 

  12. Carlson, C.S. et al. Additional SNPs and linkage-disequilibrium analyses are necessary for whole-genome association studies in humans. Nat. Genet. 33, 518–521 (2003).

    Article  CAS  PubMed  Google Scholar 

  13. Reich, D. & Patterson, N. Will admixture mapping work to find disease genes? Phil. Trans. R. Soc. Lond. B 360, 1605–1607 (2005).

    Article  CAS  Google Scholar 

  14. Kurtzke, J.F., Beebe, G.W. & Norman, J.E. Jr. Epidemiology of multiple sclerosis in U.S. veterans: 1. Race, sex, and geographic distribution. Neurology 29, 1228–1235 (1979).

    Article  CAS  PubMed  Google Scholar 

  15. Wallin, M.T., Page, W.F. & Kurtzke, J.F. Multiple sclerosis in US veterans of the Vietnam era and later military service: race, sex, and geography. Ann. Neurol. 55, 65–71 (2004).

    Article  PubMed  Google Scholar 

  16. The Transatlantic Multiple Sclerosis Genetics Cooperative. A meta-analysis of genomic screens in multiple sclerosis. A meta-analysis of genomic screens in multiple sclerosis. Mult. Scler. 7, 3–11 (2001).

  17. 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).

    Article  CAS  PubMed  Google Scholar 

  18. Hoggart, C.J. et al. Control of confounding of genetic associations in stratified populations. Am. J. Hum. Genet. 72, 1492–1504 (2003).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. 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).

    Article  CAS  PubMed  Google Scholar 

  20. Kelly, M.A. et al. An investigation of HLA-encoded genetic susceptibility to multiple sclerosis in subjects of Asian Indian and Afro-Caribbean ethnic origin. Tissue Antigens 45, 197–202 (1995).

    Article  CAS  PubMed  Google Scholar 

  21. Lauer, K. The risk of multiple sclerosis in the U.S.A. in relation to sociogeographic features: a factor-analytic study. J. Clin. Epidemiol. 47, 43–48 (1994).

    Article  CAS  PubMed  Google Scholar 

  22. International Multiple Sclerosis Genetics Consortium. A high-density screen for linkage in multiple sclerosis. Am. J. Hum. Genet. 77, 454–467 (2005).

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

    Article  CAS  PubMed  Google Scholar 

  24. McDonald, W.I. et al. Recommended diagnostic criteria for multiple sclerosis: guidelines from the International Panel on the Diagnosis of Multiple Sclerosis. Ann. Neurol. 50, 121–127 (2001).

    Article  CAS  PubMed  Google Scholar 

  25. Poser, C.M. et al. New diagnostic criteria for multiple sclerosis: guidelines for research protocols. Ann. Neurol. 13, 227–231 (1983).

    Article  CAS  PubMed  Google Scholar 

  26. Hosono, S. et al. Unbiased whole-genome amplification directly from clinical samples. Genome Res. 13, 954–964 (2003).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Fan, J.B. et al. Highly parallel SNP genotyping. Cold Spring Harb. Symp. Quant. Biol. 68, 69–78 (2003).

    Article  CAS  PubMed  Google Scholar 

  28. Tang, K. et al. Chip-based genotyping by mass spectrometry. Proc. Natl. Acad. Sci. USA 96, 10016–10020 (1999).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Excoffier, L. & Slatkin, M. Maximum-likelihood estimation of molecular haplotype frequencies in a diploid population. Mol. Biol. Evol. 12, 921–927 (1995).

    CAS  PubMed  Google Scholar 

  30. Kong, X. et al. A combined linkage-physical map of the human genome. Am. J. Hum. Genet. 75, 1143–1148 (2004).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

We thank the individuals with multiple sclerosis, their families and friends and others for permission to use their DNA in these studies; M. Williams for his role in building the collection of samples from African Americans; B. Henderson and S. Ingles for permission to use data from the prostate cancer study; D. Goldstein for frequency information from the Italian and Norwegian samples; and D. Goldstein and E. Lander for providing comments throughout the project. This work was supported by grants to D.R. from the Wadsworth Foundation and the US National Institute of Neurological Disorders and Stroke; to D.A.H. from the National Multiple Sclerosis Society; to J.R.O. from the US National Institute of Neurological Disorders and Stroke and the National Multiple Sclerosis Society; and to S.L.H. from the Nancy David and Montel Williams Foundations. D.R. is the recipient of a Burroughs-Wellcome Career Development Award in the Biomedical Sciences; B.A.C.C. is a Sylvia Lawry fellow of the National Multiple Sclerosis Society; N.P. has a US National Institutes of Health career transition award; D.A.H. has a US National Institute of Neurological Disorders and Stroke Javits Investigator Award; and P.L.D.J. is the William C Fowler scholar for MS Research and is supported by US National Institute of Neurological Disorders and Stroke grant and the Clinical Investigator Training program: Harvard-MIT Health Sciences and Technology–Beth Israel Deaconess Medical Center, in collaboration with Pfizer, Inc.

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Authors and Affiliations

  1. Department of Genetics, Harvard Medical School, Boston, Massachusetts, USA

    David Reich, Gavin J McDonald, Arti Tandon & Scott A Fruhan

  2. Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, Massachusetts, USA

    David Reich, Nick Patterson, Philip L De Jager, Gavin J McDonald, Alicja Waliszewska, Arti Tandon, Scott A Fruhan & David A Hafler

  3. Department of Neurology, Harvard Medical School, Boston, Massachusetts, USA

    Philip L De Jager, Alicja Waliszewska, Scott A Fruhan & David A Hafler

  4. Laboratory of Molecular Immunology, Center for Neurologic Disease, Brigham and Women's Hospital, Boston, Massachusetts, USA

    Philip L De Jager, Alicja Waliszewska, Scott A Fruhan & David A Hafler

  5. Department of Neurology, University of California, San Francisco, California, USA

    Robin R Lincoln, Cari DeLoa, Bruce A C Cree, Stephen L Hauser & Jorge R Oksenberg

  6. Clinique Neurologique, L'Hôpital, Quitman, Fort-de-France, France

    Philippe Cabre & Odile Bera

  7. Laboratoire Universitaire d'Immunologie, University de Rennes 1 and Etablissement Français du Sang Bretagne, Rennes, France

    Gilbert Semana

  8. Department of Medicine, Division of Medical Sciences, University Hospital, Birmingham, UK

    M Ann Kelly & David A Francis

  9. Division of SeraCare Life Sciences Inc, Genomics Collaborative, Cambridge, Massachusetts, USA

    Kristin Ardlie

  10. Department of Neurology, Multiple Sclerosis Center, Wayne State University School of Medicine, Detroit, Michigan, USA

    Omar Khan

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  1. David Reich
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Correspondence to David Reich.

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Supplementary information

Supplementary Table 1

Results for the 1,166 SNPs used in the main MCMC analysis (analysis from Run 3). (XLS 435 kb)

Supplementary Table 2

Proof that case-control statistic is normally distributed. (PDF 44 kb)

Supplementary Table 3

Admixture statistics for the populations under study. (PDF 48 kb)

Supplementary Table 4

389 SNPs dropped from main analysis based on the linkage disequilibrium criteria. (XLS 77 kb)

Supplementary Table 5

Results by sample (analysis from run #10). (XLS 435 kb)

Supplementary Methods (PDF 127 kb)

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Reich, D., Patterson, N., Jager, P. et al. A whole-genome admixture scan finds a candidate locus for multiple sclerosis susceptibility. Nat Genet 37, 1113–1118 (2005). https://doi.org/10.1038/ng1646

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