METAINTER: Meta-Analysis of multiple regression models in genome-wide association studies

Abstract

Motivation: Meta-Analysis of summary statistics is an essential approach to guarantee the success of genome-wide association studies (GWAS). Application of the fixed or random effects model to single-marker association tests is a standard practice. More complex methods of meta-Analysis involving multiple parameters have not been used frequently, a gap that could be explained by the lack of a respective meta-Analysis pipeline. Meta-Analysis based on combining p-values can be applied to any association test. However, to be powerful, meta-Analysis methods for high-dimensional models should incorporate additional information such as study-specific properties of parameter estimates, their effect directions, standard errors and covariance structure. Results: We modified 'method for the synthesis of linear regression slopes' recently proposed in the educational sciences to the case of multiple logistic regression, and implemented it in a meta-Analysis tool called METAINTER. The software handles models with an arbitrary number of parameters, andcan directly be applied to analyze the results of single-SNP tests, global haplotype tests, tests for and under gene-gene or gene-environment interaction. Via simulations for two-single nucleotide polymorphisms (SNP) models we have shown that the proposed meta-Analysis method has correct type I error rate. Moreover, power estimates come close to that of the joint analysis of the entire sample. We conducted a real data analysis of six GWAS of type 2 diabetes, available from dbGaP (http://www.ncbi.nlm.nih.gov/gap). For each study, a genome-wide interaction analysis of all SNP pairs was performed by logistic regression tests. The results were then metaanalyzed with METAINTER.