Publication: The choice of a genetic model in the meta-analysis of molecular association studies
2
Issued Date
2005-12-01
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ISSN
14643685
03005771
03005771
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2-s2.0-29244445375
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Mahidol University
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SCOPUS
Bibliographic Citation
International Journal of Epidemiology. Vol.34, No.6 (2005), 1319-1328
Suggested Citation
Cosetta Minelli, John R. Thompson, Keith R. Abrams, Ammarin Thakkinstian, John Attia The choice of a genetic model in the meta-analysis of molecular association studies. International Journal of Epidemiology. Vol.34, No.6 (2005), 1319-1328. doi:10.1093/ije/dyi169 Retrieved from: https://repository.li.mahidol.ac.th/handle/123456789/16712
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Title
The choice of a genetic model in the meta-analysis of molecular association studies
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Abstract
Background: To evaluate gene-disease associations, genetic epidemiologists collect information on the disease risk in subjects with different genotypes (for a bi-allelic polymorphism: gg, Gg, GG). Meta-analyses of such studies usually reduce the problem to a single comparison, either by performing two separate pairwise comparisons or by assuming a specific underlying genetic model (recessive, co-dominant, dominant). A biological justification for the choice of the genetic model is seldom available. Methods: We present a genetic model-free approach, which does not assume that the underlying genetic model is known in advance but still makes use of the information available on all genotypes. The approach uses ORGG, the odds ratio between the homozygous genotypes, to capture the magnitude of the genetic effect, and λ, the heterozygote log odds ratio as a proportion of the homozygote log odds ratio, to capture the genetic mode of inheritance. The analysis assumes that the same unknown genetic model, i.e. the same λ, applies in all studies, and this is investigated graphically. The approach is illustrated using five examples of published meta-analyses. Results: Analyses based on specific genetic models can produce misleading estimates of the odds ratios when an inappropriate model is assumed. The genetic model-free approach gives appropriately wider confidence intervals than genetic model-based analyses because it allows for uncertainty about the genetic model. In terms of assessment of model fit, it performs at least as well as a bivariate pairwise analysis in our examples. Conclusions: The genetic model-free approach offers a unified approach that efficiently estimates the genetic effect and the underlying genetic model. A bivariate pairwise analysis should be used if the assumption of a common genetic model across studies is in doubt. © The Author 2005; all rights reserved.