Power and Sample Size Calculations for Genetic Association Studies in the Presence of Genetic Model Misspecification

When analyzing data from large-scale genetic association studies, such as targeted or genome-wide resequencing studies, it is common to assume a single genetic model, such as dominant or additive, for all tests of association between a given genetic variant and the phenotype. However, for many variants, the chosen model will result in poor model fit and may lack statistical power due to model misspecification.

We develop power and sample size calculations for tests of gene and gene × environment interaction, allowing for misspecification of the true mode of genetic susceptibility.

The power calculations are based on a likelihood ratio test framework and are implemented in an open-source R package ("genpwr").

We use these methods to develop an analysis plan for a resequencing study in idiopathic pulmonary fibrosis and show that using a 2-degree of freedom test can increase power to detect recessive genetic effects while maintaining power to detect dominant and additive effects.

Understanding the impact of model misspecification can aid in study design and developing analysis plans that maximize power to detect a range of true underlying genetic effects. In particular, these calculations help identify when a multiple degree of freedom test or other robust test of association may be advantageous.