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- Title
Power and Sample Size Calculations for Genetic Association Studies in the Presence of Genetic Model Misspecification.
- Authors
Moore, Camille M.; Jacobson, Sean A.; Fingerlin, Tasha E.
- Abstract
Introduction: 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. Objective: 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. Methods: The power calculations are based on a likelihood ratio test framework and are implemented in an open-source R package ("genpwr"). Results: 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. Conclusions: 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.
- Subjects
GENETIC models; IDIOPATHIC pulmonary fibrosis; LIKELIHOOD ratio tests; STATISTICAL power analysis; DEGREES of freedom; GENOTYPE-environment interaction
- Publication
Human Heredity, 2019, Vol 84, Issue 6, p256
- ISSN
0001-5652
- Publication type
Article
- DOI
10.1159/000508558