TFE 2011-2012 (final year project)

Power of genome-wide interaction association studies

Most common human diseases with a genetic component are likely to have complex etiologies. Both family-based and population-based methods have become popular for association analysis, not in the least because of ever reducing genotyping costs.

An important concept in study design and interpretation of analysis results is power. This concept is closely related to the concept of type I error. In the context of genetic association studies, it is the probability that the test statistic indicates that the observed marker loci are near an (unobserved) disease locus. Many factors may affect power of an association study, such as disease allele frequency, marker choice, effect size, misclassification errors, phenotype errors, genotype errors, study design, linkage disequilibrium between disease and marker locus, the presence of epistasis, etc. (Gordon and Finch 2005). Flexible tools to assess the power of an epistasis screening (epistasis = gene-gene interactions), are largely lacking or inadequate for particular problems at hand.

Many software packages or scripts are available for pre- and/or post-hoc power calculations, in the context of genetic association studies. This project aims to perform a literature review on the latest techniques to compute power under several scenarios and a web search on available code for power calculations – in particular those in the context of epistasis screening. There is a clear need for such an overview in the scientific community.

Hence, when carried out well, this project can result in a first scientific publication, in which the way is paved to a new, user-friendly, yet flexible, power calculator, unifying the best of all worlds…


  • Presentation of K Van Steen about power in genetic association studies with key references and a first overview of factors determining power in these studies (consult K Van Steen)
  • Gordon D, Finch SJ. Factors affecting statistical power in the detection of genetic association. J Clin Invest. 2005;115:1408–1418