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

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Auteur
Zintzaras, E.
Date
2010
DOI
10.2202/1544-6115.1542
Sujet
odds ratio
generalized odds ratio
gene
polymorphism
multiallelic
genetic
contrast
model
association study
genetic association
disease
progression
meta-analysis
CROSS CLASSIFICATIONS
DISEASE
POLYMORPHISMS
Biochemistry & Molecular Biology
Mathematical & Computational Biology
Statistics & Probability
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Résumé
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.
URI
http://hdl.handle.net/11615/34928
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