The Generalized Odds Ratio as a Measure of Genetic Risk Effect in the Analysis and Meta-Analysis of Association Studies
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Elias Zintzaras
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.
©2011 Walter de Gruyter GmbH & Co. KG, Berlin/Boston
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- Testing for Gene-Gene Interaction with AMMI Models
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- Informative or Noninformative Calls for Gene Expression: A Latent Variable Approach
- Detecting Genotyping Error Using Measures of Degree of Hardy-Weinberg Disequilibrium
- Optimisation of HMM Topologies Enhances DNA and Protein Sequence Modelling
- The Apportionment of Total Genetic Variation by Categorical Analysis of Variance
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- An Empirical Bayesian Method for Estimating Biological Networks from Temporal Microarray Data
- Parameter Estimation in Multiple-Hidden I.I.D. Models from Biological Multiple Alignment
- Asymptotic Distribution of the "Orthogonal" Quantitative Transmission Disequilibrium Test in a Structured Population: Exact Formula
- Comparing Spatial Maps of Human Population-Genetic Variation Using Procrustes Analysis
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- Buckley-James Boosting for Survival Analysis with High-Dimensional Biomarker Data
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