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Applying a new generation of genetic maps to understand human inflammatory disease

Abstract

The sequencing of the human genome and the intense study of its variation in different human populations have improved our understanding of the genome's architecture. It is now becoming clear that segments of the genome that are unbroken by reshuffling or recombination during meiosis create a mosaic of DNA 'haplotype blocks'. Here, we discuss the advantages and limitations of this block structure. Haplotype blocks hold the promise of reducing the complexity of analysing the human genome for association with disease. But can they deliver on this promise? First generation maps of these block patterns, such as the admixture and haplotype maps, are now emerging and, it is to be hoped, will accelerate the discovery of alleles that contribute to susceptibility to human inflammatory diseases.

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Figure 1: Outline of an admixture study of multiple sclerosis.
Figure 2: SNPs, haplotypes and haplotype-tagging SNPs.

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References

  1. Risch, N. J. Searching for genetic determinants in the new millennium. Nature 405, 847–856 (2000).

    Article  CAS  Google Scholar 

  2. The International HapMap Consortium. The International HapMap Project. Nature 426, 789–796 (2003).

  3. Abecasis, G. R. et al. Extent and distribution of linkage disequilibrium in three genomic regions. Am. J. Hum. Genet. 68, 191–197 (2001).

    Article  CAS  Google Scholar 

  4. 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  Google Scholar 

  5. Botstein, D. & Risch, N. Discovering genotypes underlying human phenotypes: past successes for mendelian disease, future approaches for complex disease. Nature Genet. 33 (Suppl.), 228–237 (2003).

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  7. Fessel, W. J. Systemic lupus erythematosus in the community. Incidence, prevalence, outcome, and first symptoms; the high prevalence in black women. Arch. Intern. Med. 134, 1027–1035 (1974).

    Article  CAS  Google Scholar 

  8. Hopkinson, N. D., Doherty, M. & Powell, R. J. Clinical features and race-specific incidence/prevalence rates of systemic lupus erythematosus in a geographically complete cohort of patients. Ann. Rheum. Dis. 53, 675–680 (1994).

    Article  CAS  Google Scholar 

  9. Johnson, A. E., Gordon, C., Palmer, R. G. & Bacon, P. A. The prevalence and incidence of systemic lupus erythematosus in Birmingham, England. Relationship to ethnicity and country of birth. Arthritis Rheum. 38, 551–558 (1995).

    Article  CAS  Google Scholar 

  10. Nossent, J. C. Systemic lupus erythematosus on the Caribbean island of Curacao: an epidemiological investigation. Ann. Rheum. Dis. 51, 1197–1201 (1992).

    Article  CAS  Google Scholar 

  11. Molokhia, M. et al. Relation of risk of systemic lupus erythematosus to west African admixture in a Caribbean population. Hum. Genet. 112, 310–318 (2003).

    CAS  PubMed  Google Scholar 

  12. Bonilla, C., Shriver, M. D., Parra, E. J., Jones, A. & Fernandez, J. R. Ancestral proportions and their association with skin pigmentation and bone mineral density in Puerto Rican women from New York city. Hum. Genet. 115, 57–68 (2004).

    Article  Google Scholar 

  13. 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  Google Scholar 

  14. Sachidanandam, R. et al. A map of human genome sequence variation containing 1.42 million single nucleotide polymorphisms. Nature 409, 928–933 (2001).

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  16. 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  Google Scholar 

  17. Wang, J. Maximum-likelihood estimation of admixture proportions from genetic data. Genetics 164, 747–765 (2003).

    PubMed  PubMed Central  Google Scholar 

  18. Zhu, X., Cooper, R. S. & Elston, R. C. Linkage analysis of a complex disease through use of admixed populations. Am. J. Hum. Genet. 74, 1136–1153 (2004).

    Article  CAS  Google Scholar 

  19. Gabriel, S. B. et al. The structure of haplotype blocks in the human genome. Science 296, 2225–2229 (2002).

    Article  CAS  Google Scholar 

  20. Ardlie, K. G., Lunetta, K. L. & Seielstad, M. Testing for population subdivision and association in four case–control studies. Am. J. Hum. Genet. 71, 304–311 (2002).

    Article  CAS  Google Scholar 

  21. Freedman, M. L. et al. Assessing the impact of population stratification on genetic association studies. Nature Genet. 36, 388–393 (2004).

    Article  CAS  Google Scholar 

  22. Reich, D. E. & Goldstein, D. B. Detecting association in a case–control study while correcting for population stratification. Genet. Epidemiol. 20, 4–16 (2001).

    Article  CAS  Google Scholar 

  23. Siddiqui, A. et al. Association of multidrug resistance in epilepsy with a polymorphism in the drug-transporter gene ABCB1. N. Engl. J. Med. 348, 1442–1448 (2003).

    Article  CAS  Google Scholar 

  24. Yunis, E. J. et al. Inheritable variable sizes of DNA stretches in the human MHC: conserved extended haplotypes and their fragments or blocks. Tissue Antigens 62, 1–20 (2003).

    Article  CAS  Google Scholar 

  25. Jersild, C. et al. Histocompatibility-linked immune-response determinants in multiple sclerosis. Transplant Proc. 5, 1791–1796 (1973).

    CAS  PubMed  Google Scholar 

  26. Allcock, R. J. et al. The MHC haplotype project: a resource for HLA-linked association studies. Tissue Antigens 59, 520–521 (2002).

    Article  CAS  Google Scholar 

  27. Rioux, J. D. et al. Genomewide search in Canadian families with inflammatory bowel disease reveals two novel susceptibility loci. Am. J. Hum. Genet. 66, 1863–1870 (2000).

    Article  CAS  Google Scholar 

  28. Daly, M. J. & Rioux, J. D. New approaches to gene hunting in IBD. Inflamm. Bowel Dis. 10, 312–317 (2004).

    Article  Google Scholar 

  29. Johnson, G. C. et al. Haplotype tagging for the identification of common disease genes. Nature Genet. 29, 233–237 (2001).

    Article  CAS  Google Scholar 

  30. Patil, N. et al. Blocks of limited haplotype diversity revealed by high-resolution scanning of human chromosome 21. Science 294, 1719–1723 (2001).

    Article  CAS  Google Scholar 

  31. Daly, M. J., Rioux, J. D., Schaffner, S. F., Hudson, T. J. & Lander, E. S. High-resolution haplotype structure in the human genome. Nature Genet. 29, 229–232 (2001).

    Article  CAS  Google Scholar 

  32. Dawson, E. et al. A first-generation linkage disequilibrium map of human chromosome 22. Nature 418, 544–548 (2002).

    Article  CAS  Google Scholar 

  33. Osier, M. V. et al. A global perspective on genetic variation at the ADH genes reveals unusual patterns of linkage disequilibrium and diversity. Am. J. Hum. Genet. 71, 84–99 (2002).

    Article  CAS  Google Scholar 

  34. Wall, J. D. & Pritchard, J. K. Haplotype blocks and linkage disequilibrium in the human genome. Nature Rev. Genet. 4, 587–597 (2003).

    Article  CAS  Google Scholar 

  35. Florez, J. C. et al. Haplotype structure and genotype–phenotype correlations of the sulfonylurea receptor and the islet ATP-sensitive potassium channel gene region. Diabetes 53, 1360–1368 (2004).

    Article  CAS  Google Scholar 

  36. Wall, J. D. & Pritchard, J. K. Assessing the performance of the haplotype block model of linkage disequilibrium. Am. J. Hum. Genet. 73, 502–515 (2003).

    Article  CAS  Google Scholar 

  37. Wang, N., Akey, J. M., Zhang, K., Chakraborty, R. & Jin, L. Distribution of recombination crossovers and the origin of haplotype blocks: the interplay of population history, recombination, and mutation. Am. J. Hum. Genet. 71, 1227–1234 (2002).

    Article  CAS  Google Scholar 

  38. Phillips, M. S. et al. Chromosome-wide distribution of haplotype blocks and the role of recombination hot spots. Nature Genet. 33, 382–387 (2003).

    Article  CAS  Google Scholar 

  39. Zhang, K. et al. Randomly distributed crossovers may generate block-like patterns of linkage disequilibrium: an act of genetic drift. Hum. Genet. 113, 51–59 (2003).

    PubMed  Google Scholar 

  40. Pe'er, I. & Beckmann, J. S. On the applicability of a haplotype map to un-assayed populations. Hum. Genet. 114, 214–217 (2004).

    Article  Google Scholar 

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

    Article  CAS  Google Scholar 

  42. Crawford, D. C. et al. Haplotype diversity across 100 candidate genes for inflammation, lipid metabolism, and blood pressure regulation in two populations. Am. J. Hum. Genet. 74, 610–622 (2004).

    Article  CAS  Google Scholar 

  43. Reich, D. E. et al. Linkage disequilibrium in the human genome. Nature 411, 199–204 (2001).

    Article  CAS  Google Scholar 

  44. Zhang, K., Deng, M., Chen, T., Waterman, M. S. & Sun, F. A dynamic programming algorithm for haplotype block partitioning. Proc. Natl Acad. Sci. USA 99, 7335–7339 (2002).

    Article  CAS  Google Scholar 

  45. Anderson, E. C. & Novembre, J. Finding haplotype block boundaries by using the minimum-description-length principle. Am. J. Hum. Genet. 73, 336–354 (2003).

    Article  CAS  Google Scholar 

  46. Cardon, L. R. & Abecasis, G. R. Using haplotype blocks to map human complex trait loci. Trends Genet. 19, 135–140 (2003).

    Article  CAS  Google Scholar 

  47. Stumpf, M. P. & Goldstein, D. B. Demography, recombination hotspot intensity, and the block structure of linkage disequilibrium. Curr. Biol. 13, 1–8 (2003).

    Article  CAS  Google Scholar 

  48. Clark, A. G. Finding genes underlying risk of complex disease by linkage disequilibrium mapping. Curr. Opin. Genet. Dev. 13, 296–302 (2003).

    Article  CAS  Google Scholar 

  49. Tishkoff, S. A. & Verrelli, B. C. Role of evolutionary history on haplotype block structure in the human genome: implications for disease mapping. Curr. Opin. Genet. Dev. 13, 569–575 (2003).

    Article  CAS  Google Scholar 

  50. Carlson, C. S. et al. Selecting a maximally informative set of single-nucleotide polymorphisms for association analyses using linkage disequilibrium. Am. J. Hum. Genet. 74, 106–120 (2004).

    Article  CAS  Google Scholar 

  51. Deloukas, P. & Bentley, D. The HapMap Project and its application to genetic studies of drug response. Pharmacogenomics J. 4, 88–90 (2004).

    Article  CAS  Google Scholar 

  52. Taillon-Miller, P. et al. Linkage disequilibrium maps constructed with common SNPs are useful for first-pass disease association screens. Genomics 84, 899–912 (2004).

    Article  CAS  Google Scholar 

  53. van den Oord, E. J. & Neale, B. M. Will haplotype maps be useful for finding genes? Mol. Psychiatry 9, 227–236 (2004).

    Article  CAS  Google Scholar 

  54. Goldstein, D. B., Ahmadi, K. R., Weale, M. E. & Wood, N. W. Genome scans and candidate gene approaches in the study of common diseases and variable drug responses. Trends Genet. 19, 615–622 (2003).

    Article  CAS  Google Scholar 

  55. Mitra, N. et al. Localization of cancer susceptibility genes by genome-wide single-nucleotide polymorphism linkage-disequilibrium mapping. Cancer Res. 64, 8116–8125 (2004).

    Article  CAS  Google Scholar 

  56. Maron, R. et al. Genetic susceptibility or resistance to autoimmune encephalomyelitis in MHC congenic mice is associated with differential production of pro- and anti-inflammatory cytokines. Int. Immunol. 11, 1573–1580 (1999).

    Article  CAS  Google Scholar 

  57. Greve, B. et al. The diabetes susceptibility locus Idd5.1 on mouse chromosome 1 regulates ICOS expression and modulates murine experimental autoimmune encephalomyelitis. J. Immunol. 173, 157–163 (2004).

    Article  CAS  Google Scholar 

  58. Jeffreys, A. J., Kauppi, L. & Neumann, R. Intensely punctate meiotic recombination in the class II region of the major histocompatibility complex. Nature Genet. 29, 217–222 (2001).

    Article  CAS  Google Scholar 

  59. Walsh, E. C. et al. An integrated haplotype map of the human major histocompatibility complex. Am. J. Hum. Genet. 73, 580–590 (2003).

    Article  CAS  Google Scholar 

  60. Sawcer, S. J. et al. Enhancing linkage analysis of complex disorders: an evaluation of high-density genotyping. Hum. Mol. Genet. 13, 1943–1949 (2004).

    Article  Google Scholar 

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Correspondence to David A. Hafler.

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DATABASES

Entrez Gene

IBD5

OMIM

IBD

MS

SLE

type 1 diabetes

FURTHER INFORMATION

David Hafler's laboratory

International HapMap Project

Locus View

SNP Consortium

Glossary

ADMIXTURE

The mixing of two genetically distinct populations.

ASSOCIATION STUDIES

An approach to gene mapping that looks for associations between a particular disease phenotype and allelic variation.

RANDOM GENETIC DRIFT

The random fluctuation in population allele frequencies as genes are transmitted from one generation to the next.

SINGLE-NUCLEOTIDE POLYMORPHISMS

(SNPs). Bi-allelic (typically) base-pair substitutions, which are the most common forms of genetic polymorphism.

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Hafler, D., Jager, P. Applying a new generation of genetic maps to understand human inflammatory disease. Nat Rev Immunol 5, 83–91 (2005). https://doi.org/10.1038/nri1532

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