The Generalized Odds Ratio as a Measure of Genetic Risk Effect in the Analysis and Meta-Analysis of Association Studies

Zintzaras Elias
Additional contact information
Zintzaras Elias: University of Thessaly School of Medicine and Tufts University School of Medicine

Statistical Applications in Genetics and Molecular Biology, 2010, vol. 9, issue 1, 14

Abstract: The significance of risk effects in genetic association studies is assessed using the odds ratio for various genetic models (dominant, recessive and co-dominant) by merging genotypes. These models are not independent and there is no a priori biological justification for their choice. Consequently, the interpretation of their results can be problematic, especially when multiallelic variants and disease progression are investigated. The introduction of the generalized odds ratio (ORG) may be a remedy. The ORG utilizes the complete genotype distribution and it provides an estimate of the magnitude of the association, given that the mutational load and/or the phenotype are treated as a graded exposure and/or outcome. The performance of the ORG was tested in 13 meta-analyses with binary outcomes (12 with biallelic and one 3-allelic variants) and in one meta-analysis that investigated disease progression. Six biallelic meta-analyses produced a significant ORG, indicating higher risk of disease given that the diseased subjects have a higher mutational load compared to the non-diseased ones. Four of the six meta-analyses showed significance for all genetic models. The multiallelic meta-analysis produced a significant ORG, indicating that the mutational load is implicated in disease susceptibility; on the contrary, the multiple genetic models produced diverse results. In the disease progression meta-analysis, the risk of progression was related to mutational load of the variant whereas the conventional analysis did not reveal this association. Application of the ORG could overcome the shortcomings of multiple model testing or erroneous model specification and provides an alternative and robust way for genetic association testing.

Keywords: odds ratio; generalized odds ratio; gene; polymorphism; multiallelic; genetic; contrast; model; association study; genetic association; disease; progression; meta-analysis (search for similar items in EconPapers)
Date: 2010
References: View complete reference list from CitEc
Citations: View citations in EconPapers (4)

Downloads: (external link)
https://doi.org/10.2202/1544-6115.1542 (text/html)
For access to full text, subscription to the journal or payment for the individual article is required.

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:bpj:sagmbi:v:9:y:2010:i:1:n:21

Ordering information: This journal article can be ordered from
https://www.degruyter.com/journal/key/sagmb/html

DOI: 10.2202/1544-6115.1542

Access Statistics for this article

Statistical Applications in Genetics and Molecular Biology is currently edited by Michael P. H. Stumpf

More articles in Statistical Applications in Genetics and Molecular Biology from De Gruyter
Bibliographic data for series maintained by Peter Golla ().