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The power of generalized odds ratio in assessing association in genetic studies with known mode of inheritance

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  • Elias Zintzaras

Abstract

The generalized odds ratio (OR G ) is a novel model-free approach to test the association in genetic studies by estimating the overall risk effect based on the complete genotype distribution. However, the power of OR G has not been explored and, particularly, in a setting where the mode of inheritance is known. A population genetics model was simulated in order to define the mode of inheritance of a pertinent gene--disease association in advance. Then, the power of OR G was explored based on this model and compared with the chi-square test for trend. The model considered bi- and tri-allelic gene--disease associations, and deviations from the Hardy--Weinberg equilibrium (HWE). The simulations showed that bi- and tri-allelic variants have the same pattern of power results. The power of OR G increases with increase in the frequency of mutant allele and the coefficient of selection and, of course, the degree of dominance of the mutant allele. The deviation from HWE has a considerable impact on power only for small values of the above parameters. The OR G showed superiority in power compared with the chi-square test for trend when there is deviation from HWE; otherwise, the pattern of results was similar in both the approaches.

Suggested Citation

  • Elias Zintzaras, 2012. "The power of generalized odds ratio in assessing association in genetic studies with known mode of inheritance," Journal of Applied Statistics, Taylor & Francis Journals, vol. 39(12), pages 2569-2581, August.
    • Handle: RePEc:taf:japsta:v:39:y:2012:i:12:p:2569-2581
    DOI: 10.1080/02664763.2012.722611
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    References listed on IDEAS

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    1. Ammarin Thakkinstian & John Thompson & Cosetta Minelli & John Attia, 2009. "Choosing between per-genotype, per-allele, and trend approaches for initial detection of gene-disease association," Journal of Applied Statistics, Taylor & Francis Journals, vol. 36(6), pages 633-646.
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    Cited by:

    1. J. Navarro & M. Esna-Ashari & M. Asadi & J. Sarabia, 2015. "Bivariate distributions with conditionals satisfying the proportional generalized odds rate model," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 78(6), pages 691-709, August.
    2. Anyuan Zhong & Xiaolu Xiong & Huajun Xu & Minhua Shi, 2014. "An Updated Meta-Analysis of the Association between Tumor Necrosis Factor-α -308G/A Polymorphism and Obstructive Sleep Apnea-Hypopnea Syndrome," PLOS ONE, Public Library of Science, vol. 9(9), pages 1-6, September.

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