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.

  • EELS (Ethical Economic Legal and Social) Article
  • Published:

EELS (Ethical, Economic, Legal & Social) Article

Will investments in biobanks, prospective cohorts, and markers of common patterns of variation benefit other populations for drug response and disease susceptibility gene discovery?

The Pharmacogenomics Journal volume 5, pages 75–80 (2005)Cite this article

High-throughput technologies, decreasing costs for genotyping, and genomic resources that promise to reduce the number of genotypes required to scan the genome (such as the International HapMap Project) soon will make association studies a more successful strategy for identifying genetic contributors to disease susceptibility and drug response.1, 2 Accordingly, both nonprofit funders and biotechnology companies currently are investing in infrastructure for association studies. Elements of that emerging infrastructure such as prospective cohorts and biobanks are being developed with participants from a limited number of populations with the goal of applying their findings to many other populations. However, not all phenotypes and populations may be as suitable for this approach as others. Which populations and phenotypes will benefit and which will not in the coming era of large-scale association studies?

The International HapMap Project, at US $120 million perhaps the most high-profile example of infrastructure investment in the next generation of genetic research, is intended to provide a set of markers to facilitate cost-effective association studies. To that end, the HapMap is using samples from four populations (Yoruba in Ibadan, Nigeria; Japanese in Tokyo, Japan; Han Chinese in Beijing, China; and Utah residents with ancestry from Northern and Western Europe known as the CEPH samples) to construct a genomic resource that will identify the common (>5%) haplotypes that are estimated as structuring 80–85% of all genetic variation among humans.3, 4 Preliminary research suggests that the same three to five common haplotypes will be found at any given chromosomal region in most if not all human populations, although in different frequencies.5

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

References

  1. Carlson CS, Eberle MA, Kruglyak L, Nickerson DA . Mapping complex disease loci in whole-genome association studies. Nature 2004; 429: 446–452.

    Article  CAS  Google Scholar 

  2. Goldstein DB, Ahmadi KR, Weale ME, Wood NW . Genome scans and candidate gene approaches in the study of common diseases and variable drug responses. Trends Genet 2003; 19: 615–622.

    Article  CAS  Google Scholar 

  3. Wilson JF, Weale ME, Smith AC, Gratrix F, Fletcher B, Thomas MG et al. Population genetic structure of variable drug response. Nat Genet 2001; 29: 265–269.

    Article  CAS  Google Scholar 

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

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

    Article  CAS  Google Scholar 

  6. Johnson GCL, Esposito L, Barratt BJ, Smith AN, Heward J, Di Genova G et al. Haplotype tagging for the identification of common disease genes. Nat Genet 2001; 29: 233–237.

    Article  CAS  Google Scholar 

  7. Deloukas P, Bentley D . The HapMap Project and its applications to genetic studies of drug response. Pharmacogenom J 2004; 4: 88–90.

    Article  CAS  Google Scholar 

  8. Zhao HY, Pfeiffer R, Gail MH . Haplotype analysis in population genetics and association studies. Pharmacogenomics 2003; 4: 171–178.

    Article  Google Scholar 

  9. Pritchard JK, Cox NJ . The allelic architecture of human disease genes: common disease—common variant … or not? Hum Mol Genet 2002; 11: 2417–2423.

    Article  CAS  Google Scholar 

  10. Chakravarti A . Population genetics—making sense out of sequence. Nat Genet 1999; 21: 56–60.

    Article  CAS  Google Scholar 

  11. Reich DE, Lander ES . On the allelic spectrum of human disease. Trends Genet 2001; 17: 502–510.

    Article  CAS  Google Scholar 

  12. Weiss KM, Terwilliger JD . How many diseases does it take to map a gene with SNPs? Nat Genet 2000; 26: 151–157.

    Article  CAS  Google Scholar 

  13. Clark AG . Finding genes underlying risk of complex disease by linkage disequilibrium mapping. Curr Opin Genet Dev 2003; 13: 296–302.

    Article  CAS  Google Scholar 

  14. Smith DJ, Lusis AJ . The allelic structure of common disease. Hum Mol Genet 2002; 11: 2455–2461.

    Article  CAS  Google Scholar 

  15. Zondervan KT, Cardon LR . The complex interplay among factors that influence allelic association. Nat Rev Genet 2004; 5: 89–100.

    Article  CAS  Google Scholar 

  16. Weiss KM, Clark AG . Linkage disequilibrium and the mapping of complex human traits. Trends Genet 2002; 18: 19–24.

    Article  CAS  Google Scholar 

  17. Van Den Oord EJCG, Neale BM . Will haplotype maps be useful for finding genes? Mol Psychiatry 2004; 9: 227–236.

    Article  CAS  Google Scholar 

  18. Goldstein DB . Islands of linkage disequilibrium. Nat Genet 2001; 29: 109–111.

    Article  CAS  Google Scholar 

  19. Tishkoff SA, Verrelli BC . Patterns of human genetic diversity: implications for human evolutionary history and disease. Ann Rev Genom Hum Genet 2003; 4: 293–340.

    Article  CAS  Google Scholar 

  20. Pe’er I, Beckmann JS . On the applicability of a haplotype map to un-assayed populations. Hum Genet 2004; 114: 214–217.

    Article  Google Scholar 

  21. Collins FS . The case for a US prospective cohort study of genes and environment. Nature 2004; 429: 475–477.

    Article  CAS  Google Scholar 

  22. Gulcher J, Stefansson K . Population genomics: laying the groundwork for genetic disease modeling and targeting. Clin Chem Lab Med 1998; 36: 523–527.

    Article  CAS  Google Scholar 

  23. Brown MS, Goldstein JL . A receptor-mediated pathway for cholesterol homeostasis. Science 1986; 232: 34–47.

    Article  CAS  Google Scholar 

  24. Maron DJ, Fazio S, Linton MF . Current perspectives on statins. Circulation 2000; 101: 207–213.

    Article  CAS  Google Scholar 

  25. Barbour V . UK Biobank: a project in search of a protocol? Lancet 2003; 361: 1734–1738.

    Article  Google Scholar 

  26. Wong MY, Day NE, Luan JA, Chan KP, Wareham NJ . The detection of gene–environment interaction for continuous traits: should we deal with measurement error by bigger studies or better measurement? Int J Epidemiol 2003; 32: 51–57.

    Article  CAS  Google Scholar 

  27. Weisgraber KH, Mahley RW . Human apolipoprotein E: the Alzheimer's disease connection. FASEB J 1996; 10: 1485–1494.

    Article  CAS  Google Scholar 

  28. Rosendaal FR, Koster T, Vandenbroucke JP, Reitsma PH . High-risk of thrombosis in patients homozygous for factor-V Leiden (activated protein-c resistance). Blood 1995; 85: 1504–1508.

    CAS  Google Scholar 

  29. Martinson JJ, Chapman NH, Rees DC, Liu YT, Clegg JB . Global distribution of the CCR5 gene 32-basepair deletion. Nat Genet 1997; 16: 100–103.

    Article  CAS  Google Scholar 

  30. Hirschhorn JN, Lohmueller K, Byrne E, Hirschhorn K . A comprehensive review of genetic association studies. Genet Med 2002; 4: 45–61.

    Article  CAS  Google Scholar 

  31. McCarthy MI . Progress in defining the molecular basis of type 2 diabetes mellitus through susceptibility-gene identification. Hum Mol Genet 2004; 13: R33–R41, (Sp. Iss. 1).

    Article  CAS  Google Scholar 

  32. Thompson JF, Lloyd DB, Lira ME, Milos PM . Cholesteryl ester transfer protein promoter single-nucleotide polymorphisms in Sp1-binding sites affect transcription and are associated with high-density lipoprotein cholesterol. Clin Genet 2004; 66: 223–228.

    Article  CAS  Google Scholar 

  33. Newman DL, Hoffjan S, Bourgain C, Abney M, Nicolae RI, Profits ET et al. Are common disease susceptibility alleles the same in outbred and founder populations? Eur J Hum Genet 2004; 12: 584–590.

    Article  CAS  Google Scholar 

  34. Morabia A, Cayanis E, Costanza MC, Ross BM, Bernstein MS, Flaherty MS et al. Association between lipoprotein lipase (LPL) gene and blood lipids: a common variant for a common trait? Genet Epidemiol 2003; 24: 309–321.

    Article  Google Scholar 

Download references

Acknowledgements

This publication was made possible by grant numbers ES11174 from the National Institute on Environmental Health Sciences and HG02691 the National Human Genome Research Institute. Its contents are solely the responsibility of the authors and do not necessarily represent the official views of the NIEHS, NHGRI, or the National Institutes of Health. We also are grateful for comments made by LD Brooks and an anonymous reviewer on earlier versions.

Author information

Authors and Affiliations

  1. Department of Anthropology, University of Oklahoma, Norman, OK, USA

    M W Foster

  2. Center for Medical Ethics and Health Policy, Baylor College of Medicine, Houston, TX, USA

    R R Sharp

Authors
  1. M W Foster
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. R R Sharp
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to M W Foster.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Foster, M., Sharp, R. Will investments in biobanks, prospective cohorts, and markers of common patterns of variation benefit other populations for drug response and disease susceptibility gene discovery?. Pharmacogenomics J 5, 75–80 (2005). https://doi.org/10.1038/sj.tpj.6500295

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/sj.tpj.6500295

Search

Advanced search

Quick links