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| Home > Publications Database > Meta-Analysis of Multiple Regression Models in Genome-Wide Association Studies |
| Abstract | DZNE-2020-00587 |
;
2015
Abstract: Meta-analysis refers to the statistical synthesis of the results from a series of related studies. Meta-analysis of summary statistics from Genome-wide association studies (GWAS) may lead to dis-covery of new susceptibility loci without the need to exchange gen-otype data.Well-known p-value combination methods can be applied to an arbitrary association test. However, these methods do not pro-vide summary effects and are known to be underpowered for high-dimensional models. The broadly used methods for meta-analysis of individual GWAS results address mostly one parameter models. Results of multiple regression analysis applied, for instance, to ge-nome-wide interaction analysis have rarely been used due to the complexities underlying the process of their synthesis. For high-dimensional models the gain of power can be achieved when meta-analysis methods incorporate information on study-specific properties of parameter estimates, their effect directions, standard errors and covariance structure. In this context, a method for the synthesis of regression slopes (MSRS) represents a perspec-tive approach of meta-analysis for advanced GWAS. MSRS pro-vides meta-analysis p-values and common parameter estimates of multiple regression models for an arbitrary number of parameters, and can be used to test the homogeneity of studies results. We introduce an efficient, powerful and freely available soft-ware tool METAINTER, which implements MSRS and three fur-ther meta-analysis methods: Fisher’s method, Stouffer’s method with weights and Stouffer’s method with weights and effect direc-tions. METAINTER enables meta-analysis of tests for and under gene-gene interaction, single-SNP association tests with several degrees of freedom, global haplotype tests etc. Simulation study shows that MSRS has correct type I error and its power comes close to that of the joint sample analysis. We have conducted a real data analysis of six GWAS of type 2 Diabetes. For each study, a genome-wide interaction analysis of all SNP pairs has been performed by logistic regression tests. The results have then been meta-analysed with METAINTER.
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