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.

  • Original Article
  • Published:

Admixture in Hispanic Americans: its impact on ITGAM association and implications for admixture mapping in SLE

Genes & Immunity volume 10, pages 539–545 (2009)Cite this article

Abstract

Systemic Lupus Erythematosus (SLE) disproportionately affects minorities, such as Hispanic Americans (HA). Prevalence of SLE is 3–5 times higher in HA than in European-derived populations and have more active disease at the time of diagnosis, with more serious organ system involvement. HA is an admixed population, it is possible that there is an effect of admixture on the relative risk of the disease. This admixture can create substantial increase of linkage disequilibrium (LD) in both magnitude and range, which can provide a unique opportunity for admixture mapping. The main objectives of this study are to (a) estimate hidden population structure in HA individuals; (b) estimate individual ancestry proportions and its impact on SLE risk; (c) assess impact of admixture on ITGAM association, a recently identified SLE susceptibility gene; and (d) estimate power of admixture mapping in HA. Our dataset contained 1125 individuals, of whom 884 (657 SLE cases and 227 controls) were self-classified as HA. Using 107 unlinked ancestry informative markers (AIMs), we estimated hidden population structure and individual ancestry in HA. Out of 5671 possible pairwise LD, 54% were statistically significant, indicating recent population admixture. The best-fitted model for HA was a four-population model with average ancestry of European (48%), American-Indian (AI) (40%), African (8%) and a fourth population (4%) with unknown ancestry. We also identified significant higher risk associated with AI ancestry (odds ratio (OR)=4.84, P=0.0001, 95% CI (confidence interval)=2.14–10.95) on overall SLE. We showed that ITGAM is associated as a risk factor for SLE (OR=2.06, P=8.74 × 10−5, 95% CI=1.44–2.97). This association is not affected by population substructure or admixture. We have shown that HA have great potential and are an appropriate population for admixture mapping. As expected, the case-only design is more powerful than case–control design, for any given admixture proportion or ancestry risk ratio.

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

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Figure 1
Figure 2
Figure 3

Similar content being viewed by others

References

  1. Alarcon GS, McGin Jr G, Petri M, Reveille JD, Ramsey-Goldman R, Kimberly RP, for the PROFILE Study Group. Baseline characteristics of a multiethnic lupus cohort: PROFILE. Lupus 2002; 11: 95–101.

    Article  CAS  PubMed  Google Scholar 

  2. McKeigue PM . Prospects for admixture mapping of complex traits. Am J Hum Genet 2005; 76: 1–7.

    Article  CAS  PubMed  Google Scholar 

  3. Smith MW, O'Brien SJ . Mapping by admixture linkage disequilibrium: advances, limitations and guidelines. Nature Rev Genet 2005; 6: 623–632.

    Article  CAS  PubMed  Google Scholar 

  4. Chakraborty R, Weiss KM . Frequencies of complex diseases in hybrid populations. Am J Phys Anthropol 1986; 70: 489–503.

    Article  CAS  PubMed  Google Scholar 

  5. Escalante A, Rincon ID . Epidemiology and impact on rheumatic disorders in the US Hispanic population. Curr Opin Rheumatol 2001; 13: 104–110.

    Article  CAS  PubMed  Google Scholar 

  6. Reveille JD, Moulds JM, Ahn C, Friedman AW, Baethge B, Roseman J et al. Systemic Lupus Erythematosus in three ethnic groups. I. The effects of HLA Class II, C4, and CR1 alleles, socioeconomic factors, and ethnicity at disease onset. Arthritis Rheum 1998; 41: 1161–1172.

    Article  CAS  PubMed  Google Scholar 

  7. Molina JF, Molina J, Garcia G, Gharavi AE, Wilson WA, Espinoza LR . Ethnic differences in the clinical expression of Systemic Lupus Erythematosus: a comparative study between African-Americans and Latin Americans. Lupus 1997; 6: 63–67.

    Article  CAS  PubMed  Google Scholar 

  8. Petri M, Perez-Gutthann S, Longenecker JC, Hochberg M . Morbidity of Systemic Lupus Erythematosus: role of race and socioeconomic status. Am J Med 1991; 91: 345–353.

    Article  CAS  PubMed  Google Scholar 

  9. Patterson N, Hattangadi N, Lane B, Lohmueller KE, Hafler DA, Oksenberg JR et al. Methods for high-density admixture mapping of disease genes. Am J Hum Genet 2004; 74: 979–1000.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. The International HapMap Consortium. The International HapMap Project. Nature 2003; 426: 789–796.

    Article  Google Scholar 

  11. Jakobsson M, Scholz SW, Scheet P, Gibbs JR, VanLiere JM, Fung H-C et al. Genotype, haplotype and copy-number variation in worldwide human populations. Nature 2008; 451: 998–1003.

    Article  CAS  PubMed  Google Scholar 

  12. Weir BS, Cockerham CC . Estimating F-statistics for the analysis of population structure. Evolution 1984; 38: 1358–1370.

    CAS  PubMed  Google Scholar 

  13. Nei M . Molecular Evolutionary Genetics. Columbia University Press: New York, 1987.

    Google Scholar 

  14. Seldin MF, Qi L, Scherbarth HR, Tian C, Ransom M, Silva G et al. Amerindian ancestry in Argentina is associated with increased risk for Systemic Lupus Erythematosus. Genes Immun 2008; 9: 389–393.

    Article  CAS  PubMed  Google Scholar 

  15. Nath SK, Han S, Kim-Howard X, Kelly JA, Viswanathan P, Gilkeson GS et al. A nonsynonymous functional variant in integrin-alpha(M) (encoded by ITGAM) is associated with systemic lupus erythematosus. Nat Genet 2008; 40: 152–154.

    Article  CAS  PubMed  Google Scholar 

  16. Hoggart CJ, Shriver MD, Kittles RA, Clayton DG, McKeigue PM . Design and analysis of admixture mapping studies. Am J Hum Genet 2004; 74: 965–978.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Montana G, Pritchard JK . Statistical tests for admixture mapping with case–control and cases-only data. Am J Hum Genet 2004; 75: 771–789.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Sans M . Admixture Studies in Latin America: from the 20th to the 21th century. Hum Biol 2000; 72: 155–177.

    CAS  PubMed  Google Scholar 

  19. Yang N, Li H, Criswell LA, Gregersen PK, Alarcon-Riquelme ME, Kittles R et al. Examination of ancestry and ethnic affiliation using highly informative diallelic DNA markers: application to diverse and admixed populations and implications for clinical epidemiology and forensic medicine. Hum Genet 2005; 118: 382–392.

    Article  PubMed  Google Scholar 

  20. Tian C, Hinds DA, Shigeta R, Adler SG, Lee A, Pahl MV et al. A genomewide single-nucleotide plymorphism panel for Mexican American admixture mapping. Am J Hum Genet 2007; 80: 1014–1023.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Kao WH, Klag MJ, Meoni LA, Reich D, Berthier-Shaad Y, Man L et al. MYH9 is associated with nondiabetic end-stage renal disease in African Americans. Nat Gen 2008; 40: 1185–1192.

    Article  CAS  Google Scholar 

  22. Haiman CA, Patterson N, Freedman ML, Myers SR, Pike MC, Waliszewska A et al. Multiple regions within 8q24 independently affect risk for prostate cancer. Nat Gen 2007; 39: 638–644.

    Article  CAS  Google Scholar 

  23. Price AL, Patterson N, Yu F, Cox DR, Waliszewska A, McDonald GJ et al. A genomewide admixture map for Latino populations. Am J Hum Genet 2007; 80: 1024–1036.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Mao X, Bigham AW, Mei R, Gutierrez G, Weiss KM, Brutsaert TD et al. A Genomewide admixture mapping panel for Hispanic/Latino populations. Am J Hum Genet 2007; 80: 1171–1178.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Tan EM, Cohen AS, Fries JF, Masi AT, McShane DJ, Rothfield NF et al. The 1982 revised criteria for the classification of Systemic Lupus Erythematosus. Arthritis Rheum 1982; 25: 1271–1277.

    Article  CAS  PubMed  Google Scholar 

  26. Hochberg MC . Updating the American College of Rheumatology revised criteria for the classification of systemic lupus erythematosus. Arthritis Rheum 1997; 40: 1725–1734.

    Article  CAS  PubMed  Google Scholar 

  27. Harley JB, Alarcón-Riquelme ME, Criswell LA, Jacob CO, Kimberly RP, Moser KL et al. Genome-wide association scan in women with systemic lupus erythematosus identifies susceptibility variants in ITGAM, PXK, KIAA1542 and other loci. International Consortium for Systemic Lupus Erythematosus Genetics (SLEGEN). Nat Genet 2008; 40: 204–210.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Purcell S, Neale B, Todd-Brown K, Thomas L, Ferreira MAR, Bender D et al. PLINK: a toolset for whole-genome association and population-based linkage analysis. Am J Hum Genet 2007; 81: 559–575.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Goudet J . FSTAT (Version 1.2): a computer program to calculate F-statistics. J Hered 1995; 86: 485–486.

    Article  Google Scholar 

  30. Gabriel SB, Schaffner SF, Nguyen H, Moore JM, Roy J, Blumenstiel B et al. Structure of Haplotype blocks in the human genome. Science 2002; 296: 2225–2229.

    Article  CAS  PubMed  Google Scholar 

  31. Reich DE, Cargill M, Bolk S, Ireland J, Sabeti PC, Richter DJ et al. Linkage disequilibrium in the human genome. Nature 2001; 411: 199–204.

    Article  CAS  PubMed  Google Scholar 

  32. Choudhry S, Coyle NE, Tang H, Salari K, Lind D, Clark SL et al. Population stratification confounds genetic association of studies among Latinos. Hum Genet 2006; 118: 652–664.

    Article  PubMed  Google Scholar 

  33. Pritchard J, Stephens M, Rosenberg NA, Donnelly P . Association mapping in structured populations. Am J Hum Genet 2000; 67: 170–181.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Falush D, Stephens M, Pritchard JK . Inference of population structure using multilocus genotype data: linked loci and correlated allele frequencies. Genetics 2003; 164: 1567–1587.

    CAS  PubMed  PubMed Central  Google Scholar 

  35. Pritchard J, Stephens M, Donelly P . Inference of population structure using multilocus genotype data. Genetics 2000; 155: 945–959.

    CAS  PubMed  PubMed Central  Google Scholar 

  36. Aulchenko YS, Ripke S, Isaacs A, van Duijn CM . GenABEL: an R library for genome-wide association analysis. Bioinformatics 2007; 23: 1294–1296.

    Article  CAS  PubMed  Google Scholar 

  37. Evanno G, Regnaut S, Goudet J . Detecting the number of clusters of individuals using the software STRUCTURE: a simulation study. Mol Ecol 2005; 14: 2611–2620.

    Article  CAS  PubMed  Google Scholar 

  38. SAS [Computer program]. Version 9.1. SAS InstituteCary, NC, 2008.

  39. Review Manager (RevMan) [Computer program]. Version 5.0. The Nordic Cochrane Centre, The Cochrane Collaboration: Copenhagen, 2008.

Download references

Acknowledgements

We wish to thank the patients and their families for their cooperation and blood samples. We also like to acknowledge the support from LFRR staffs, especially, Gail Bruner, Jennifer Kelly, Dr Jashua Ojwang, Dr Ken Kaufman for conducting a large-scale genotyping project from which the current samples were genotyped. In addition, we would like to thank Dr Judith James and Dr Gary Gilkerson for providing genotypic data on some Hispanic samples used in this study. This study was made possible by funding from AR42460, AR12253, AR48940, Alliance for Lupus Research, RO1A1063622, PO1AR049084, P30AR053483 and P20RR020143.

Author information

Authors and Affiliations

  1. Genetic Epidemiology Unit, Oklahoma Medical Research Foundation, Oklahoma City, OK, USA

    J E Molineros, X Kim-Howard, H Deshmukh & S K Nath

  2. Arthritis and Immunology Research Program, Oklahoma Medical Research Foundation, Oklahoma City, OK, USA

    J E Molineros, X Kim-Howard, H Deshmukh, J B Harley & S K Nath

  3. Department of Molecular Microbiology and Immunology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA

    C O Jacob

  4. Oklahoma University Health Sciences Center, Oklahoma City, OK, USA

    J B Harley & S K Nath

  5. VA medical Center, Oklahoma City, OK, USA

    J B Harley

Authors
  1. J E Molineros
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. X Kim-Howard
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. H Deshmukh
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. C O Jacob
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. J B Harley
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. S K Nath
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to S K Nath.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Molineros, J., Kim-Howard, X., Deshmukh, H. et al. Admixture in Hispanic Americans: its impact on ITGAM association and implications for admixture mapping in SLE. Genes Immun 10, 539–545 (2009). https://doi.org/10.1038/gene.2009.30

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/gene.2009.30

Keywords

This article is cited by

Search

Advanced search

Quick links