Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
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.

  • Letter
  • Published:

Replication validity of genetic association studies

Nature Genetics volume 29, pages 306–309 (2001)Cite this article

Abstract

The rapid growth of human genetics creates countless opportunities for studies of disease association. Given the number of potentially identifiable genetic markers and the multitude of clinical outcomes to which these may be linked, the testing and validation of statistical hypotheses in genetic epidemiology is a task of unprecedented scale1,2. Meta-analysis provides a quantitative approach for combining the results of various studies on the same topic, and for estimating and explaining their diversity3,4. Here, we have evaluated by meta-analysis 370 studies addressing 36 genetic associations for various outcomes of disease. We show that significant between-study heterogeneity (diversity) is frequent, and that the results of the first study correlate only modestly with subsequent research on the same association. The first study often suggests a stronger genetic effect than is found by subsequent studies. Both bias and genuine population diversity might explain why early association studies tend to overestimate the disease protection or predisposition conferred by a genetic polymorphism. We conclude that a systematic meta-analytic approach may assist in estimating population-wide effects of genetic risk factors in human disease.

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

Access options

Buy this article

  • Purchase on SpringerLink
  • Instant access to full article PDF
Buy now

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

Figure 1: Correlation between the odds ratio (OR) in the first study/studies and in subsequent research.
Figure 2: Evolution of the strength of an association as more information is accumulated.

Similar content being viewed by others

References

  1. McCarthy, J.J. & Hilfiker, R. The use of single-nucleotide polymorphism maps in pharmacogenomics. Nature Biotechnol. 18, 505–508 (2000).

    Article  CAS  Google Scholar 

  2. Khoury, M.J. & Little, J. Human genome epidemiology reviews: the beginning of something HuGE. Am. J. Epidemiol. 151, 2–3 (2000).

    Article  CAS  Google Scholar 

  3. Lau, J., Ioannidis, J.P.A. & Schmid, C.H. Summing up evidence: one answer is not always enough. Lancet 351, 123–127 (1998).

    Article  CAS  Google Scholar 

  4. Mosteller, F. & Colditz, G.A. Understanding research synthesis (meta-analysis). Annu. Rev. Public Health 17, 1–23 (1996).

    Article  CAS  Google Scholar 

  5. Lau, J., Ioannidis, J.P. & Schmid, C.H. Quantitative synthesis in systematic reviews. Ann. Intern. Med. 127, 820–826 (1997).

    Article  CAS  Google Scholar 

  6. Mantel, N. & Haenszel, W. Statistical aspects of the analysis of data from retrospective studies of disease. J. Natl Cancer Inst. 22, 719–748 (1959).

    CAS  PubMed  Google Scholar 

  7. DerSimonian, R. & Laird, N. Meta-analysis in clinical trials. Control. Clin. Trials 7, 177–188 (1986).

    Article  CAS  Google Scholar 

  8. Hedges, L.V. & Olkin, I. Statistical Methods for Meta-analysis (Academic, Orlando, 1985).

    Google Scholar 

  9. Cooper, H. & Hedges, L.V. (eds.) The Handbook of Research Synthesis (Russell Sage Foundation, New York, 1994).

    Google Scholar 

  10. Ioannidis, J.P., Contopoulos-Ioannidis, D.G. & Lau, J. Recursive cumulative meta-analysis: a diagnostic for the evolution of total randomized evidence for group and individual patient data. J. Clin. Epidemiol. 52, 281–291 (1999).

    Article  CAS  Google Scholar 

  11. Ioannidis, J.P. & Lau, J. Evolution of treatment effects over time: empirical insight from recursive cumulative meta-analyses. Proc. Natl Acad. Sci. USA 98, 831–836 (2001).

    Article  CAS  Google Scholar 

  12. Gershon, E.S. & Cloninger, C.R. (eds) Genetic Approaches to Mental Disorders (American Psychiatric Press, Washington, DC, 1994).

    Google Scholar 

  13. Vieland, V.J., Wang, K. & Huang, J. Power to detect linkage based on multiple sets of data in the presence of locus heterogeneity: comparative evaluation of model-based linkage methods for affected sib pair data. Hum. Hered. 51, 199–208 (2001).

    Article  CAS  Google Scholar 

  14. Greenberg, D.A. Summary of analyses of problem 2 simulated data for GAW11. Genet. Epidemiol. 17 (Suppl. 1), S429–447 (1999).

    Article  Google Scholar 

  15. Wang, K., Vieland, V. & Huang, J. A Bayesian approach to replication of linkage findings. Genet. Epidemiol. 17, S749–754 (1999).

    Article  Google Scholar 

  16. Gu, C. Province, M. & Rao, D.C. Meta-analysis of genetic linkage to quantitative trait loci with study-specific covariates: a mixed-effects model. Genet. Epidemiol. 17 (Suppl. 1), S599–604 (1999).

    Article  Google Scholar 

  17. Oxman, A.D. & Guyatt, G.H. A consumer's guide to subgroup analyses. Ann. Intern. Med. 116, 78–84 (1992).

    Article  CAS  Google Scholar 

  18. Ioannidis, J.P. & Lau, J. Uncontrolled pearls, controlled evidence, meta-analysis, and the individual patient. J. Clin. Epidemiol. 51, 709–711 (1998).

    Article  CAS  Google Scholar 

  19. Yudkin, P.L. & Stratton, I.M. How to deal with regression to the mean in intervention studies. Lancet 347, 241–243 (1996).

    Article  CAS  Google Scholar 

  20. Easterbrook, P.J., Berlin, J.A., Gopalan, R. & Matthews, D.R. Publication bias in clinical research. Lancet 337, 867–872 (1991).

    Article  CAS  Google Scholar 

  21. Ioannidis, J.P. Effect of the statistical significance of results on the time to completion and publication of randomized efficacy trials. J. Am. Med. Assoc. 279, 281–286 (1998).

    Article  CAS  Google Scholar 

  22. Cappelleri, J.C. et al. Large trials and meta-analysis of smaller trials: how do their results compare? J. Am. Med. Assoc. 276, 1332–1338 (1996).

    Article  CAS  Google Scholar 

  23. Hosmer, D.W. & Lemeshow, S. Applied Logistic Regression (John Wiley & Sons, New York, 1989).

    Google Scholar 

Download references

Acknowledgements

This work was supported in part by a grant from the General Secretariat for Research and Technology, Greece, funded through the European Union.

Author information

Authors and Affiliations

  1. Department of Hygiene and Epidemiology, Clinical and Molecular Epidemiology Unit and Clinical Trials and Evidence-Based Medicine Unit, University of Ioannina School of Medicine, Ioannina, 45110, Greece

    John P.A. Ioannidis, Evangelia E. Ntzani, Thomas A. Trikalinos & Despina G. Contopoulos-Ioannidis

  2. Ioannina Biomedical Research Institute, Ioannina, 45110, Greece

    John P.A. Ioannidis

  3. Department of Medicine, Tufts University School of Medicine, Boston, 02111, Massachusetts, USA

    John P.A. Ioannidis

  4. Department of Pediatrics, George Washington University School of Medicine and Health Sciences, Washington DC, 20010, USA

    Despina G. Contopoulos-Ioannidis

Authors
  1. John P.A. Ioannidis
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Evangelia E. Ntzani
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Thomas A. Trikalinos
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Despina G. Contopoulos-Ioannidis
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to John P.A. Ioannidis.

Supplementary information

Rights and permissions

Reprints and permissions

About this article

Cite this article

Ioannidis, J., Ntzani, E., Trikalinos, T. et al. Replication validity of genetic association studies. Nat Genet 29, 306–309 (2001). https://doi.org/10.1038/ng749

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/ng749

This article is cited by

Search

Advanced 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