Size matters: Just how big is BIG?: Quantifying realistic sample size requirements for human genome epidemiology

Paul R. Burton*, Anna L. Hansell, Isabel Fortier, Teri A. Manolio, Muin J. Khoury, Julian Little, Paul Elliott

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

189 Citations (Scopus)

Abstract

Background: Despite earlier doubts, a string of recent successes indicates that if sample sizes are large enough, it is possible - both in theory and in practice - to identify and replicate genetic associations with common complex diseases. But human genome epidemiology is expensive and, from a strategic perspective, it is still unclear what "large enough" really means. This question has critical implications for governments, funding agencies, bioscientists and the tax-paying public. Difficult strategic decisions with imposing price tags and important opportunity costs must be taken. Methods: Conventional power calculations for case-control studies disregard many basic elements of analytic complexity - e.g. errors in clinical assessment, and the impact of unmeasured aetiological determinants - and can seriously underestimate true sample size requirements. This article describes, and applies, a rigorous simulation-based approach to power calculation that deals more comprehensively with analytic complexity and has been implemented on the web as ESPRESSO: (www.p3gobservatory.org/powercalculator.htm). Results: Using this approach, the article explores the realistic power profile of stand-alone and nested case-control studies in a variety of settings and provides a robust quantitative foundation for determining the required sample size both of individual biobanks and of large disease-based consortia. Despite universal acknowledgment of the importance of large sample sizes, our results suggest that contemporary initiatives are still, at best, at the lower end of the range of desirable sample size. Insufficient power remains particularly problematic for studies exploring gene-gene or gene-environment interactions. Discussion: Sample size calculation must be both accurate and realistic, and we must continue to strengthen national and international cooperation in the design, conduct, harmonization and integration of studies in human genome epidemiology.

Original languageEnglish
Pages (from-to)263-273
Number of pages11
JournalInternational Journal of Epidemiology
Volume38
Issue number1
DOIs
Publication statusPublished - 2009
Externally publishedYes

Bibliographical note

Funding Information:
We gratefully acknowledge the support of the steering committee of UK Biobank in encouraging and discussing the implications of this research. Initial power calculations were funded by UK Biobank from its joint funders: Wellcome Trust, Medical Research Council, Department of Health, Scottish Executive and Northwest Regional Development Agency. This work was also supported as a central element of the research programmes of P3G (the Public Population Project in Genomics) funded by Genome Canada and Genome Quebec, and PHOEBE (Promoting Harmonization of Epidemiological Biobanks in Europe) funded by the European Union under the Framework 6 program. A.L.H. is a Wellcome Trust Intermediate Clinical Fellow (grant number 075883). J.L. is a Canada Research Chair in Human Genome Epidemiology. The programme of methods research in genetic epidemiology in Leicester is funded in part by MRC Cooperative Grant G9806740. We wish to thank those who kindly provided us with advice and data: Gabriele Nagel, Sabine Rohrman, Bertrand Hemon, Paolo Vineis [European Prospective Investigation of Cancer and Nutrition (EPIC)]; Peter Rothwell (Stroke Prevention Research Unit, Radcliffe Infirmary, Oxford); Joan Soriano, GlaxoSmithKline (for estimates of UK COPD incidence) and the UK Small Area Health Statistics Unit, Imperial College London.

Keywords

  • Aetiological heterogeneity
  • Biobank
  • Human genome epidemiology
  • Measurement error
  • Reliability
  • Sample size
  • Simulation studies
  • Statistical power

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