A Multivariate Method for Meta-Analysis of Studies Investigating Gene-Environment InteractionsThe potential interaction between susceptibility genotypes (G) and environmental exposures (E) has recently received a lot of attention in epidemiologic studies for understanding the etiology of complex human diseases. Statistical assessment of the GxE interaction can be performed in nontraditional case-only designs achieving a great statistical power improvement over traditional case-control/cohort designs studies. Although the standard methods of meta-analysis can be applied to assess GxE interaction, the fact that the available studies have to follow all the same design remains a major problem. In the present study we propose a novel multivariate method for meta-analysis that can incorporate studies with different type of data performed under different designs. Based on the proposed method neither individual patient data nor the simultaneous evaluation of both genetic and environmental effect are necessary. The method is simple and fast and can be extended to account for multiple genes resulting thus in the estimation of GxG interactions. The assumptions inherently made by different designs (e.g. combining case-only and case-control studies) can also be assessed. As an example we investigate the interaction of NAT2 polymorphism and smoking in bladder cancer illustrating that the multidimensional multivariate methods outperformed the classical univariate analysis. We conclude that the proposed method constitutes a useful framework for performing meta-analysis for GxE interaction.International Journal Publications
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