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

Abstract

<b><i>Introduction:</i></b> 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. <b><i>Objective:</i></b> 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. <b><i>Methods:</i></b> The power calculations are based on a likelihood ratio test framework and are implemented in an open-source R package (“genpwr”). <b><i>Results:</i></b> 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. <b><i>Conclusions:</i></b> 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.