UK Biobank: An Open Access Resource for Identifying the Causes of a Wide Range of Complex Diseases of Middle and Old Age

The UK Biobank is a major UK collaborative research project to recruit and follow longitudinally the health of 500,000 volunteers aged between 40-69 years. It will provide important biological samples and environmental exposure data. As such, it will constitute a resource for many future investigations of the separate and combined effects of genetic, environmental and lifestyle factors on human morbidity, mortality and health.

Genetic association studies are central to efforts to identify and characterise genomic variants underlying susceptibility to multifactorial disease. However, obtaining robust replication of initial association findings has proved difficult. Much of this inconsistency can be attributed to inadequacies in study design, implementation, and interpretation--inadequately powered sample groups are a major concern. Several additional factors affect the quality of any given association study, with appropriate sample-recruitment strategy, logical variant selection, minimum genotyping error, relevant data analysis, and valid interpretation all essential to generation of robust findings. Replication has a vital role in showing that associations that are identified reflect interesting biological processes rather than methodological quirks. For an unbiased view of the evidence for and against any particular association, study quality, rather than significance value, needs to play the dominant part.

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.