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

Elias Zintzaras

Journal of Applied Statistics, 2012, vol. 39, issue 12, 2569-2581

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.

Date: 2012
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
http://hdl.handle.net/10.1080/02664763.2012.722611 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:japsta:v:39:y:2012:i:12:p:2569-2581

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/CJAS20

DOI: 10.1080/02664763.2012.722611

Access Statistics for this article

Journal of Applied Statistics is currently edited by Robert Aykroyd

More articles in Journal of Applied Statistics from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().