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Cited by 84Genetic Associations and EpidemiologyStatistical Methods in EpidemiologyStatistical Methods in Clinical Trials

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

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Moore, Camille M., Jacobson, Sean A., Fingerlin, Tasha E.·2019·Human Heredity
DOI 10.1159/000508558PMC PMC7666027
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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.

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  • For the common variant pQTL analysis power calculations with n = 500, the study assumed linear regression with an additive genetic model with no gene-gene interactions using genpwr, following the strategy previously used by Yao et al.

    “For the common variant pQTL analysis power calculations with n = 500, we assumed linear regression with an additive genetic model with no gene–gene interactions using genpwr (version 1.0.4) R package33, following the strategy previously used by Yao et al.34.”

    From Genetic determinants of plasma protein levels in the Estonian population (2024) · cites this paper as [33]

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Cited by

Sorted by citations

Authors
Moore, C. M., Jacobson, S. A., & Fingerlin, T. E.
Affiliations
National Jewish Health (US)
Corresponding author
Moore, Camille M.
ORCID
Venue
Human Heredity
Publisher
S. Karger AG
Published
2019
Volume
84
Issue
6
Pages
256-271
ISSN
  • 0001-5652 (ISSN-L)
  • 1423-0062
DOI
10.1159/000508558
PMC
PMC7666027
OpenAlex
W3045676119
Cited by
84
References
2
Citations by year
2026: 9 · 2025: 12 · 2024: 22 · 2023: 15 · 2022: 20 · 2021: 5 · 2020: 1
FWCI
4.02
Type
journal article
Language
en
Open access
Licence
Locations
  • Human Heredity (publishedVersion)
  • PubMed (publishedVersion)
  • PubMed Central (acceptedVersion)
Funders
National Heart, Lung, and Blood Institute (R01HL097163, T32HL007085)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
  • Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Epidemiology
  • Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Clinical Trials
Keywords
power and sample size calculation, genetic association studies, gene-environment interaction, dominant genetic effects, additive genetic effects, likelihood ratio test, idiopathic pulmonary fibrosis, whole-genome resequencing, research design

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