Power and Sample Size Computation for Genetic Association Studies of Binary Traits: Accounting for Covariate Effects
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Power and sample size computation plays an important role in the design and analysis of genetic association studies. Unlike when analyzing a continuous trait, the power of association testing between a binary trait and a genetic variant is influenced by covariate effect sizes, in addition to the genetic effect size. Motivated by this phenomenon, we thus propose and implement a unified methodology for power and sample size computation that can account for the presence of covariate effects of different structures. Extensive simulation studies show that the proposed method is accurate and computationally efficient for both prospective and retrospective sampling designs with various covariate structures. A proof-of-principle application to the UK Biobank data, focusing on the understudied African sample, shows that ignoring the covariate age effect leads to overestimated power (underestimated replication sample size) when analyzing the binary hypertension trait, while the computation for the continuous blood pressure trait is invariant to covariate effect size.
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