A multipoint method for meta-analysis of genetic association studies
| Autor: | Theodore D. Liakopoulos, Pantelis G. Bagos |
|---|---|
| Rok vydání: | 2010 |
| Předmět: |
Linkage disequilibrium
Multivariate statistics Epidemiology Population Angiotensinogen CX3C Chemokine Receptor 1 Coronary Artery Disease Polymorphism Single Nucleotide Linkage Disequilibrium Meta-Analysis as Topic Statistics Genetic model Odds Ratio Humans International HapMap Project education Genetics (clinical) Mathematics Genetic association Molecular Epidemiology education.field_of_study Polymorphism Genetic Models Genetic Meta-analysis Hypertension Multivariate Analysis Receptors Chemokine Pairwise comparison Genome-Wide Association Study |
| Zdroj: | Genetic Epidemiology. 34:702-715 |
| ISSN: | 0741-0395 |
| Popis: | Meta-analyses of genetic association studies are usually performed using a single polymorphism at a time, even though in many cases the individual studies report results from partially overlapping sets of polymorphisms. We present here a multipoint (or multilocus) method for multivariate meta-analysis of published population-based case-control association studies. The method is derived by extending the general method for multivariate meta-analysis and allows for multivariate modelling of log(odds ratios (OR)) derived from several polymorphisms that are in linkage disequilibrium (LD). The method is presented in a genetic model-free approach, although it can also be used by assuming a genetic model of inheritance beforehand. Furthermore, the method is presented in a unified framework and is easily applied to both discrete outcomes (using the OR), as well as to meta-analyses of a continuous outcome (using the mean difference). The main innovation of the method is the analytical calculation of the within-studies covariances between estimates derived from linked polymorphisms. The only requirement is that of an external estimate for the degree of pairwise LD between the polymorphisms under study, which can be obtained from the same published studies, from the literature or from HapMap. Thus, the method is quite simple and fast, it can be extended to an arbitrary set of polymorphisms and can be fitted in nearly all statistical packages (Stata, R/Splus and SAS). Applications in two already published meta-analyses provide encouraging results concerning the robustness and the usefulness of the method and we expect that it would be widely used in the future. Genet. Epidemiol. 34: 702-715, 2010. © 2010 Wiley-Liss, Inc. |
| Databáze: | OpenAIRE |
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