deMeta: Removing sub-studies from meta-analysis of genome wide association studies (GWAS)

ABSTRACT Summary Post-GWAS studies using the results from large consortium meta-analysis often need to correctly take care of the overlapping sample issue. The gold standard approach for resolving this issue is to reperform the GWAS or meta-analysis excluding the overlapped participants. However, such approach is time-consuming and, sometimes, restricted by the available data. deMeta provides a user friendly and computationally efficient command-line implementation for removing the effect of a contributing sub-study to a consortium from the meta-analysis results. Only the summary statistics of the meta-analysis the sub-study to be removed are required. In addition, deMeta can generate contrasting Manhattan and quantile-quantile plots for users to visualize the impact of the sub-study on the meta-analysis results. Availability and Implementation The python source code, examples and documentations of deMeta are publicly available at https://github.com/Computational-NeuroGenetics/deMeta-beta . Contact [email protected] (J. Sun); [email protected] (Y. Wang) Supplementary information None.